HR Data Analyis
# Clear variables in memory
rm(list=ls())
# Read csv file
HR <- read.csv("HRSub2.csv", head = TRUE, sep = ",")
head(HR)## MarriedID MaritalDesc Sex EmploymentStatus Department PerfScoreID
## 1 0 Single M Active Production 4
## 2 1 Married M Voluntarily Terminated IT/IS 3
## 3 1 Married F Voluntarily Terminated Production 3
## 4 1 Married F Active Production 3
## 5 0 Divorced F Voluntarily Terminated Production 3
## 6 0 Single F Active Production 4
## RecruitmentSource Salary Position State Age CitizenDesc
## 1 LinkedIn 62506 Production Technician I MA 38 US Citizen
## 2 Indeed 104437 Sr. DBA MA 46 US Citizen
## 3 LinkedIn 64955 Production Technician II MA 33 US Citizen
## 4 Indeed 64991 Production Technician I MA 33 US Citizen
## 5 Google Search 50825 Production Technician I MA 32 US Citizen
## 6 LinkedIn 57568 Production Technician I MA 44 US Citizen
## RaceDesc HispanicLatino EmployedYear ManagerName EngagementSurvey
## 1 White No 10 Michael Albert 4.60
## 2 White No 1 Simon Roup 4.96
## 3 White No 1 Kissy Sullivan 3.02
## 4 White No 13 Elijiah Gray 4.84
## 5 White No 5 Webster Butler 5.00
## 6 White No 9 Amy Dunn 5.00
## EmpSatisfaction SpecialProjectsCount DaysLateLast30 Absences
## 1 5 0 0 1
## 2 3 6 0 17
## 3 3 0 0 3
## 4 5 0 0 15
## 5 4 0 0 2
## 6 5 0 0 15#Import Libraries
library(glmnet)## Loading required package: Matrix## Loaded glmnet 4.1-3library(caret)## Loading required package: ggplot2## Loading required package: latticelibrary(car)## Loading required package: carDatalibrary(CombMSC)##
## Attaching package: 'CombMSC'## The following object is masked from 'package:car':
##
## subsets## The following object is masked from 'package:stats':
##
## BIClibrary(ggplot2)Preprocessing Data
# Correct Errors
HR$HispanicLatino[188]="Yes"
HR$HispanicLatino[98]="No"
# Factorization
HR$MarriedID = as.factor(HR$MarriedID)
HR$MaritalDesc = as.factor(HR$MaritalDesc)
HR$Sex = as.factor(HR$Sex)
HR$EmploymentStatus = as.factor(HR$EmploymentStatus)
HR$Department = as.factor(HR$Department)
HR$RecruitmentSource = as.factor(HR$RecruitmentSource)
HR$Position = as.factor(HR$Position)
HR$State = as.factor(HR$State)
HR$CitizenDesc = as.factor(HR$CitizenDesc)
HR$RaceDesc = as.factor(HR$RaceDesc)
HR$HispanicLatino = as.factor(HR$HispanicLatino)
HR$ManagerName = as.factor(HR$ManagerName)Split the Data set in training and testing data
set.seed(123)
train_index = createDataPartition(HR$Salary, p = 0.7, list = FALSE, times = 1)
HRtrain = HR[train_index,]
HRtest = HR[-train_index,]
# Deal with the data absence in training dataset
# Department
test_unique = unique(HRtest$Department)
train_unique = unique(HRtrain$Department)
for (i in test_unique){
if ((i %in% train_unique)==FALSE){
add_row = HRtest[HRtest$Department == i,]
HRtrain = rbind(HRtrain, add_row)
}
}
# Position
test_unique = unique(HRtest$Position)
train_unique = unique(HRtrain$Position)
for (i in test_unique){
if ((i %in% train_unique)==FALSE){
add_row = HRtest[HRtest$Position == i,]
HRtrain = rbind(HRtrain, add_row)
}
}
# State
test_unique = unique(HRtest$State)
train_unique = unique(HRtrain$State)
for (i in test_unique){
if ((i %in% train_unique)==FALSE){
add_row = HRtest[HRtest$State == i,]
HRtrain = rbind(HRtrain, add_row)
}
}Exploratory Data Analysis - Boxplots
ggplot(HR, aes(x=MarriedID, y=Salary, color=MarriedID)) +
geom_boxplot() + theme(legend.position="none")
ggplot(HR, aes(x=MaritalDesc, y=Salary, color=MaritalDesc)) +
geom_boxplot()+ theme(legend.position="none")
ggplot(HR, aes(x=Sex, y=Salary, color=Sex)) +
geom_boxplot()+ theme(legend.position="none")
ggplot(HR, aes(x=EmploymentStatus, y=Salary, color=EmploymentStatus)) +
geom_boxplot()+ theme(legend.position="none")
ggplot(HR, aes(x=Department, y=Salary, color=Department)) +
geom_boxplot() + scale_x_discrete(guide = guide_axis(n.dodge=3)) + theme(legend.position="none")
ggplot(HR, aes(x=RecruitmentSource, y=Salary, color=RecruitmentSource)) +
geom_boxplot() + scale_x_discrete(guide = guide_axis(n.dodge=3)) + theme(legend.position="none")
ggplot(HR, aes(x=Position, y=Salary, color=Position)) +
geom_boxplot() + theme(axis.text.x = element_text(size = 8, angle = 90, vjust = 0.5, hjust=1)) + theme(legend.position="none")
ggplot(HR, aes(x=State, y=Salary, color=State)) +
geom_boxplot() + scale_x_discrete(guide = guide_axis(n.dodge=3)) + theme(legend.position="none")
ggplot(HR, aes(x=CitizenDesc, y=Salary, color=CitizenDesc)) +
geom_boxplot() + theme(legend.position="none")
ggplot(HR, aes(x=RaceDesc, y=Salary, color=RaceDesc)) +
geom_boxplot() + scale_x_discrete(guide = guide_axis(n.dodge=3)) + theme(legend.position="none")
ggplot(HR, aes(x=HispanicLatino, y=Salary, color=HispanicLatino)) +
geom_boxplot() + theme(legend.position="none")
ggplot(HR, aes(x=ManagerName, y=Salary, color=ManagerName)) +
geom_boxplot()+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + theme(legend.position="none")
Exploratory Data Analysis - Scatter plots
ggplot(HR, aes(x=PerfScoreID, y=Salary)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HR, aes(x=Age, y=Salary)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HR, aes(x=EmployedYear, y=Salary)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HR, aes(x=EngagementSurvey, y=Salary)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HR, aes(x=EmpSatisfaction, y=Salary)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HR, aes(x=SpecialProjectsCount, y=Salary)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HR, aes(x=DaysLateLast30, y=Salary)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HR, aes(x=Absences, y=Salary)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
4.Fitting multiple linear regression model
model_full = lm(Salary~., data = HRtrain)
summary(model_full)##
## Call:
## lm(formula = Salary ~ ., data = HRtrain)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12622 -2976 0 2552 14399
##
## Coefficients: (17 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55366.43 14407.74 3.843 0.000188
## MarriedID1 -4019.09 1967.99 -2.042 0.043120
## MaritalDescMarried NA NA NA NA
## MaritalDescSeparated -4298.44 3076.28 -1.397 0.164672
## MaritalDescSingle -2257.31 2018.56 -1.118 0.265479
## MaritalDescWidowed 901.80 4024.41 0.224 0.823040
## SexM -1148.62 1084.22 -1.059 0.291356
## EmploymentStatusTerminated for Cause 2319.14 3046.13 0.761 0.447811
## EmploymentStatusVoluntarily Terminated -502.85 2025.02 -0.248 0.804272
## DepartmentExecutive Office 188703.22 9811.72 19.232 < 2e-16
## DepartmentIT/IS 33519.41 6145.41 5.454 2.34e-07
## DepartmentProduction -508.20 6757.24 -0.075 0.940163
## DepartmentSales 3319.06 12018.50 0.276 0.782855
## DepartmentSoftware Engineering 18592.05 7588.33 2.450 0.015592
## PerfScoreID -1297.39 1255.53 -1.033 0.303333
## RecruitmentSourceDiversity Job Fair -328.25 2822.80 -0.116 0.907603
## RecruitmentSourceEmployee Referral 1853.98 2801.93 0.662 0.509331
## RecruitmentSourceGoogle Search -1848.36 2341.71 -0.789 0.431341
## RecruitmentSourceIndeed 360.94 2272.72 0.159 0.874060
## RecruitmentSourceLinkedIn -1137.82 2230.91 -0.510 0.610886
## RecruitmentSourceOn-line Web application -4900.59 7131.07 -0.687 0.493152
## RecruitmentSourceOther -6152.01 9416.82 -0.653 0.514699
## RecruitmentSourceWebsite -66.92 3628.90 -0.018 0.985314
## PositionAdministrative Assistant -10164.70 5419.19 -1.876 0.062907
## PositionArea Sales Manager 4369.69 8930.83 0.489 0.625454
## PositionBI Developer 353.21 5871.08 0.060 0.952118
## PositionBI Director 17383.47 7750.87 2.243 0.026579
## PositionCIO 134696.72 12165.97 11.072 < 2e-16
## PositionData Analyst -6705.53 5384.64 -1.245 0.215225
## PositionData Architect 50616.53 8047.09 6.290 4.31e-09
## PositionDatabase Administrator 14623.15 5820.64 2.512 0.013200
## PositionDirector of Operations 117463.69 11659.20 10.075 < 2e-16
## PositionDirector of Sales 130646.14 16100.20 8.115 2.98e-13
## PositionEnterprise Architect 13755.84 8135.56 1.691 0.093229
## PositionIT Director 79393.36 8211.38 9.669 < 2e-16
## PositionIT Manager - DB 48144.48 6483.24 7.426 1.24e-11
## PositionIT Manager - Infra 68587.35 9076.81 7.556 6.17e-12
## PositionIT Manager - Support 42326.11 8094.08 5.229 6.50e-07
## PositionIT Support -23914.80 5805.62 -4.119 6.67e-05
## PositionNetwork Engineer -31375.42 5938.09 -5.284 5.09e-07
## PositionPresident & CEO NA NA NA NA
## PositionPrincipal Data Architect 30791.79 8077.83 3.812 0.000211
## PositionProduction Manager 27171.40 9578.78 2.837 0.005279
## PositionProduction Technician I -10021.35 1322.85 -7.576 5.56e-12
## PositionProduction Technician II NA NA NA NA
## PositionSales Manager NA NA NA NA
## PositionSenior BI Developer -9511.49 7756.28 -1.226 0.222271
## PositionShared Services Manager 42742.68 11071.79 3.861 0.000176
## PositionSoftware Engineer 15946.97 7113.14 2.242 0.026637
## PositionSoftware Engineering Manager NA NA NA NA
## PositionSr. Accountant 43071.39 7844.92 5.490 1.98e-07
## PositionSr. DBA 11078.68 7797.73 1.421 0.157745
## PositionSr. Network Engineer NA NA NA NA
## StateAZ -4881.38 9260.76 -0.527 0.599006
## StateCA 5854.83 9778.30 0.599 0.550361
## StateCO -2776.61 9530.70 -0.291 0.771254
## StateCT -1133.89 8903.97 -0.127 0.898860
## StateFL 773.44 9992.58 0.077 0.938421
## StateGA -1860.74 9200.97 -0.202 0.840046
## StateID -2315.86 9888.72 -0.234 0.815199
## StateIN -8172.85 9669.81 -0.845 0.399533
## StateKY -6348.05 10171.75 -0.624 0.533648
## StateMA 5384.69 8926.34 0.603 0.547387
## StateME -12102.07 10259.82 -1.180 0.240297
## StateMT -9214.54 9789.78 -0.941 0.348301
## StateNC 254.51 10021.32 0.025 0.979777
## StateND 679.11 10299.78 0.066 0.947530
## StateNH 6509.68 10614.81 0.613 0.540757
## StateNV -4127.29 9989.23 -0.413 0.680149
## StateNY 4809.30 10018.25 0.480 0.631984
## StateOH -14750.10 11180.00 -1.319 0.189343
## StateOR -12184.46 9668.69 -1.260 0.209821
## StatePA 9076.84 13126.84 0.691 0.490483
## StateRI NA NA NA NA
## StateTN 7352.60 9447.74 0.778 0.437821
## StateTX 2556.98 9640.81 0.265 0.791250
## StateUT 3666.18 10286.15 0.356 0.722096
## StateVA 8694.92 10445.44 0.832 0.406679
## StateVT 2346.94 9637.00 0.244 0.807970
## StateWA -3867.80 10642.73 -0.363 0.716871
## Age 36.32 65.28 0.556 0.578936
## CitizenDescNon-Citizen 4423.59 5897.54 0.750 0.454545
## CitizenDescUS Citizen 435.22 3235.64 0.135 0.893205
## RaceDescAsian 1949.97 5236.97 0.372 0.710231
## RaceDescBlack or African American 2680.00 5098.22 0.526 0.599996
## RaceDescTwo or more races -1761.26 5667.60 -0.311 0.756474
## RaceDescWhite 713.04 5002.67 0.143 0.886878
## HispanicLatinoYes -3794.63 2096.07 -1.810 0.072515
## EmployedYear 118.82 330.62 0.359 0.719879
## ManagerNameAmy Dunn 4234.03 2527.19 1.675 0.096224
## ManagerNameBoard of Directors NA NA NA NA
## ManagerNameBrandon R. LeBlanc NA NA NA NA
## ManagerNameBrannon Miller 4199.45 2483.28 1.691 0.093179
## ManagerNameBrian Champaigne NA NA NA NA
## ManagerNameDavid Stanley 2549.37 2597.93 0.981 0.328237
## ManagerNameDebra Houlihan NA NA NA NA
## ManagerNameElijiah Gray 3812.96 2601.61 1.466 0.145130
## ManagerNameEric Dougall -5784.19 6324.27 -0.915 0.362068
## ManagerNameJanet King -12982.35 9028.69 -1.438 0.152829
## ManagerNameJennifer Zamora NA NA NA NA
## ManagerNameJohn Smith NA NA NA NA
## ManagerNameKelley Spirea 2660.81 2431.12 1.094 0.275738
## ManagerNameKetsia Liebig -483.80 2489.93 -0.194 0.846237
## ManagerNameKissy Sullivan 2967.80 2333.70 1.272 0.205710
## ManagerNameLynn Daneault NA NA NA NA
## ManagerNameMichael Albert 2016.44 2696.38 0.748 0.455891
## ManagerNamePeter Monroe NA NA NA NA
## ManagerNameSimon Roup NA NA NA NA
## ManagerNameWebster Butler NA NA NA NA
## EngagementSurvey 863.88 819.21 1.055 0.293572
## EmpSatisfaction 584.00 557.75 1.047 0.296985
## SpecialProjectsCount -486.80 1017.48 -0.478 0.633134
## DaysLateLast30 -399.66 604.46 -0.661 0.509645
## Absences 131.52 90.05 1.461 0.146519
##
## (Intercept) ***
## MarriedID1 *
## MaritalDescMarried
## MaritalDescSeparated
## MaritalDescSingle
## MaritalDescWidowed
## SexM
## EmploymentStatusTerminated for Cause
## EmploymentStatusVoluntarily Terminated
## DepartmentExecutive Office ***
## DepartmentIT/IS ***
## DepartmentProduction
## DepartmentSales
## DepartmentSoftware Engineering *
## PerfScoreID
## RecruitmentSourceDiversity Job Fair
## RecruitmentSourceEmployee Referral
## RecruitmentSourceGoogle Search
## RecruitmentSourceIndeed
## RecruitmentSourceLinkedIn
## RecruitmentSourceOn-line Web application
## RecruitmentSourceOther
## RecruitmentSourceWebsite
## PositionAdministrative Assistant .
## PositionArea Sales Manager
## PositionBI Developer
## PositionBI Director *
## PositionCIO ***
## PositionData Analyst
## PositionData Architect ***
## PositionDatabase Administrator *
## PositionDirector of Operations ***
## PositionDirector of Sales ***
## PositionEnterprise Architect .
## PositionIT Director ***
## PositionIT Manager - DB ***
## PositionIT Manager - Infra ***
## PositionIT Manager - Support ***
## PositionIT Support ***
## PositionNetwork Engineer ***
## PositionPresident & CEO
## PositionPrincipal Data Architect ***
## PositionProduction Manager **
## PositionProduction Technician I ***
## PositionProduction Technician II
## PositionSales Manager
## PositionSenior BI Developer
## PositionShared Services Manager ***
## PositionSoftware Engineer *
## PositionSoftware Engineering Manager
## PositionSr. Accountant ***
## PositionSr. DBA
## PositionSr. Network Engineer
## StateAZ
## StateCA
## StateCO
## StateCT
## StateFL
## StateGA
## StateID
## StateIN
## StateKY
## StateMA
## StateME
## StateMT
## StateNC
## StateND
## StateNH
## StateNV
## StateNY
## StateOH
## StateOR
## StatePA
## StateRI
## StateTN
## StateTX
## StateUT
## StateVA
## StateVT
## StateWA
## Age
## CitizenDescNon-Citizen
## CitizenDescUS Citizen
## RaceDescAsian
## RaceDescBlack or African American
## RaceDescTwo or more races
## RaceDescWhite
## HispanicLatinoYes .
## EmployedYear
## ManagerNameAmy Dunn .
## ManagerNameBoard of Directors
## ManagerNameBrandon R. LeBlanc
## ManagerNameBrannon Miller .
## ManagerNameBrian Champaigne
## ManagerNameDavid Stanley
## ManagerNameDebra Houlihan
## ManagerNameElijiah Gray
## ManagerNameEric Dougall
## ManagerNameJanet King
## ManagerNameJennifer Zamora
## ManagerNameJohn Smith
## ManagerNameKelley Spirea
## ManagerNameKetsia Liebig
## ManagerNameKissy Sullivan
## ManagerNameLynn Daneault
## ManagerNameMichael Albert
## ManagerNamePeter Monroe
## ManagerNameSimon Roup
## ManagerNameWebster Butler
## EngagementSurvey
## EmpSatisfaction
## SpecialProjectsCount
## DaysLateLast30
## Absences
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6170 on 132 degrees of freedom
## Multiple R-squared: 0.9712, Adjusted R-squared: 0.9503
## F-statistic: 46.4 on 96 and 132 DF, p-value: < 2.2e-165.Testing model assumptions
#plot(Salary~PerfScoreID+Age+EmployedYear+EngagementSurvey+EmpSatisfaction+SpecialProjectsCount+DaysLateLast30+Absences,data=HR)
resids = model_full$residuals
# Linearity
par(mfrow=c(2,2))
ggplot(HRtrain, aes(x=PerfScoreID, y=resids)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=Age, y=resids)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=EmployedYear, y=resids)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=EngagementSurvey, y=resids)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=EmpSatisfaction, y=resids)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=SpecialProjectsCount, y=resids)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=DaysLateLast30, y=resids)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=Absences, y=resids)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
# Constant Variance
plot(fitted(model_full),resids,xlab="Fitted values",ylab="Residuals")
abline(h=0, col="red")
lines(lowess(model_full$fitted.values, resids), col='blue')
# Normality
hist(resids, xlab="residuals", col="orange",main=NULL, nclass=15)
qqPlot(resids, xlab="normal quantiles", ylab="residuals")## 284 80
## 201 56
Transformation Try Outs
# Sqrt Transformation
model_full_tr = lm(sqrt(Salary)~., data = HRtrain)
# Variance
resids_tr = model_full_tr$residuals
plot(fitted(model_full_tr),resids_tr,xlab="Fitted values",ylab="Residuals")
abline(h=0, col="red")
lines(lowess(model_full_tr$fitted.values, resids_tr), col='blue')
# Normality
hist(resids_tr, xlab="residuals", col="orange",main=NULL, nclass=15)
qqPlot(resids_tr, xlab="normal quantiles", ylab="residuals")
## 284 80
## 201 56# Log Transformation
model_full_log = lm(log(Salary)~., data = HRtrain)
# Variance
resids_log = model_full_log$residuals
plot(fitted(model_full_log),resids_log,xlab="Fitted values",ylab="Residuals")
abline(h=0, col="red")
lines(lowess(model_full_log$fitted.values, resids_log), col='blue')
# Normality
hist(resids_log, xlab="residuals", col="orange",main=NULL, nclass=15)
qqPlot(resids_log, xlab="normal quantiles", ylab="residuals")
## 284 80
## 201 56Model Selection
#Forward, Stepwise Regression
reduced_model = lm(Salary~1, data = HRtrain)
full_model = lm(Salary~., data = HRtrain)
step(reduced_model,scope =list(lower= reduced_model, upper=full_model), direction="forward")## Start: AIC=4685.51
## Salary ~ 1
##
## Df Sum of Sq RSS AIC
## + Position 30 1.6708e+11 7.5203e+09 4025.3
## + ManagerName 20 1.0905e+11 6.5547e+10 4501.2
## + Department 5 8.6971e+10 8.7626e+10 4537.6
## + SpecialProjectsCount 1 4.2231e+10 1.3237e+11 4624.1
## + Age 1 5.3622e+09 1.6923e+11 4680.4
## + RecruitmentSource 8 1.4606e+10 1.5999e+11 4681.5
## + PerfScoreID 1 3.7197e+09 1.7088e+11 4682.6
## <none> 1.7460e+11 4685.5
## + Absences 1 1.5107e+09 1.7309e+11 4685.5
## + EmpSatisfaction 1 1.4749e+09 1.7312e+11 4685.6
## + EngagementSurvey 1 1.4353e+09 1.7316e+11 4685.6
## + DaysLateLast30 1 1.2974e+09 1.7330e+11 4685.8
## + HispanicLatino 1 9.6722e+08 1.7363e+11 4686.2
## + EmploymentStatus 2 2.2524e+09 1.7234e+11 4686.5
## + Sex 1 1.3346e+08 1.7446e+11 4687.3
## + EmployedYear 1 1.0951e+08 1.7449e+11 4687.4
## + MarriedID 1 6.2478e+07 1.7453e+11 4687.4
## + RaceDesc 4 4.1408e+09 1.7046e+11 4688.0
## + CitizenDesc 2 4.7506e+08 1.7412e+11 4688.9
## + MaritalDesc 4 6.7424e+08 1.7392e+11 4692.6
## + State 27 1.3895e+10 1.6070e+11 4720.5
##
## Step: AIC=4025.34
## Salary ~ Position
##
## Df Sum of Sq RSS AIC
## + EmpSatisfaction 1 174393871 7345885562 4022.0
## + Absences 1 164124350 7356155083 4022.3
## + HispanicLatino 1 89532825 7430746608 4024.6
## <none> 7520279433 4025.3
## + MarriedID 1 54001322 7466278111 4025.7
## + SpecialProjectsCount 1 52814205 7467465228 4025.7
## + Age 1 49637668 7470641766 4025.8
## + PerfScoreID 1 43649723 7476629710 4026.0
## + EngagementSurvey 1 39353574 7480925859 4026.1
## + Sex 1 37172035 7483107399 4026.2
## + DaysLateLast30 1 14813751 7505465682 4026.9
## + EmployedYear 1 1596822 7518682611 4027.3
## + MaritalDesc 4 172353147 7347926287 4028.0
## + CitizenDesc 2 34558739 7485720694 4028.3
## + EmploymentStatus 2 17518614 7502760819 4028.8
## + RaceDesc 4 115362524 7404916909 4029.8
## + RecruitmentSource 8 275209295 7245070139 4032.8
## + ManagerName 12 518554393 7001725040 4033.0
## + State 26 962543385 6557736048 4046.0
##
## Step: AIC=4021.96
## Salary ~ Position + EmpSatisfaction
##
## Df Sum of Sq RSS AIC
## + Absences 1 132069340 7213816222 4019.8
## + HispanicLatino 1 74327927 7271557635 4021.6
## <none> 7345885562 4022.0
## + Age 1 59611884 7286273678 4022.1
## + SpecialProjectsCount 1 48534335 7297351227 4022.4
## + MarriedID 1 47210469 7298675093 4022.5
## + Sex 1 28948936 7316936626 4023.1
## + EngagementSurvey 1 16944407 7328941155 4023.4
## + PerfScoreID 1 9455305 7336430257 4023.7
## + DaysLateLast30 1 1024462 7344861099 4023.9
## + EmployedYear 1 279920 7345605641 4024.0
## + MaritalDesc 4 151620317 7194265244 4025.2
## + EmploymentStatus 2 17564476 7328321086 4025.4
## + CitizenDesc 2 17292404 7328593158 4025.4
## + RaceDesc 4 116276144 7229609418 4026.3
## + RecruitmentSource 8 298358627 7047526935 4028.5
## + ManagerName 12 532107463 6813778099 4028.7
## + State 26 830713800 6515171761 4046.5
##
## Step: AIC=4019.81
## Salary ~ Position + EmpSatisfaction + Absences
##
## Df Sum of Sq RSS AIC
## + HispanicLatino 1 95501851 7118314371 4018.8
## + Age 1 73513282 7140302940 4019.5
## + MarriedID 1 67308592 7146507630 4019.7
## <none> 7213816222 4019.8
## + SpecialProjectsCount 1 43835940 7169980282 4020.4
## + Sex 1 31721858 7182094364 4020.8
## + EngagementSurvey 1 17916113 7195900109 4021.2
## + PerfScoreID 1 6301469 7207514753 4021.6
## + DaysLateLast30 1 1050524 7212765698 4021.8
## + EmployedYear 1 31998 7213784225 4021.8
## + MaritalDesc 4 166144246 7047671976 4022.5
## + CitizenDesc 2 18619891 7195196331 4023.2
## + EmploymentStatus 2 16244615 7197571608 4023.3
## + RaceDesc 4 90053971 7123762251 4024.9
## + ManagerName 12 530220322 6683595900 4026.3
## + RecruitmentSource 8 288390527 6925425695 4026.5
## + State 26 800942960 6412873262 4044.9
##
## Step: AIC=4018.76
## Salary ~ Position + EmpSatisfaction + Absences + HispanicLatino
##
## Df Sum of Sq RSS AIC
## + MarriedID 1 96619366 7021695005 4017.6
## + Age 1 61905738 7056408633 4018.8
## <none> 7118314371 4018.8
## + SpecialProjectsCount 1 44558161 7073756210 4019.3
## + Sex 1 25355950 7092958421 4019.9
## + EngagementSurvey 1 22629416 7095684955 4020.0
## + PerfScoreID 1 5989249 7112325122 4020.6
## + DaysLateLast30 1 1301144 7117013227 4020.7
## + EmployedYear 1 227252 7118087119 4020.7
## + MaritalDesc 4 168276797 6950037574 4021.3
## + CitizenDesc 2 16242187 7102072184 4022.2
## + EmploymentStatus 2 12770444 7105543926 4022.3
## + ManagerName 12 555637028 6562677343 4024.1
## + RaceDesc 4 80303711 7038010660 4024.2
## + RecruitmentSource 8 287669271 6830645100 4025.3
## + State 26 831057367 6287257004 4042.3
##
## Step: AIC=4017.63
## Salary ~ Position + EmpSatisfaction + Absences + HispanicLatino +
## MarriedID
##
## Df Sum of Sq RSS AIC
## <none> 7021695005 4017.6
## + SpecialProjectsCount 1 56726871 6964968133 4017.8
## + Age 1 53502816 6968192189 4017.9
## + Sex 1 29300674 6992394331 4018.7
## + EngagementSurvey 1 14348768 7007346237 4019.2
## + PerfScoreID 1 5234175 7016460830 4019.5
## + DaysLateLast30 1 2035095 7019659910 4019.6
## + EmployedYear 1 1409152 7020285853 4019.6
## + CitizenDesc 2 19602698 7002092307 4021.0
## + ManagerName 12 601578787 6420116218 4021.1
## + MaritalDesc 3 71657431 6950037574 4021.3
## + EmploymentStatus 2 10360781 7011334224 4021.3
## + RaceDesc 4 82289389 6939405615 4022.9
## + RecruitmentSource 8 302883314 6718811691 4023.5
## + State 26 864931192 6156763813 4039.5##
## Call:
## lm(formula = Salary ~ Position + EmpSatisfaction + Absences +
## HispanicLatino + MarriedID, data = HRtrain)
##
## Coefficients:
## (Intercept) PositionAdministrative Assistant
## 59675.3 -10431.0
## PositionArea Sales Manager PositionBI Developer
## 1054.3 31874.0
## PositionBI Director PositionCIO
## 47432.1 154215.8
## PositionData Analyst PositionData Architect
## 26402.4 85475.7
## PositionDatabase Administrator PositionDirector of Operations
## 49005.6 104428.4
## PositionDirector of Sales PositionEnterprise Architect
## 115505.2 39656.0
## PositionIT Director PositionIT Manager - DB
## 111928.4 80015.5
## PositionIT Manager - Infra PositionIT Manager - Support
## 97379.6 74605.6
## PositionIT Support PositionNetwork Engineer
## 2338.5 -1706.6
## PositionPresident & CEO PositionPrincipal Data Architect
## 190867.5 59106.1
## PositionProduction Manager PositionProduction Technician I
## 14645.5 -7928.1
## PositionProduction Technician II PositionSales Manager
## 1424.7 3314.6
## PositionSenior BI Developer PositionShared Services Manager
## 23106.7 28388.5
## PositionSoftware Engineer PositionSoftware Engineering Manager
## 31335.2 14992.2
## PositionSr. Accountant PositionSr. DBA
## 41281.0 41058.8
## PositionSr. Network Engineer EmpSatisfaction
## 29125.4 791.3
## Absences HispanicLatinoYes
## 162.7 -3107.2
## MarriedID1
## -1436.1#Backward, Stepwise Regression
step(full_model,scope = list(lower=reduced_model,upper=full_model),direction="backward")## Start: AIC=4065.02
## Salary ~ MarriedID + MaritalDesc + Sex + EmploymentStatus + Department +
## PerfScoreID + RecruitmentSource + Position + State + Age +
## CitizenDesc + RaceDesc + HispanicLatino + EmployedYear +
## ManagerName + EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
##
## Step: AIC=4065.02
## Salary ~ MarriedID + MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## RecruitmentSource + Position + State + Age + CitizenDesc +
## RaceDesc + HispanicLatino + EmployedYear + ManagerName +
## EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
##
## Step: AIC=4065.02
## Salary ~ MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## RecruitmentSource + Position + State + Age + CitizenDesc +
## RaceDesc + HispanicLatino + EmployedYear + ManagerName +
## EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - State 25 8.8616e+08 5.9115e+09 4052.2
## - RecruitmentSource 7 1.5335e+08 5.1787e+09 4057.9
## - CitizenDesc 2 2.5962e+07 5.0513e+09 4062.2
## - RaceDesc 4 1.2120e+08 5.1465e+09 4062.5
## - ManagerName 10 3.9915e+08 5.4244e+09 4062.5
## - EmploymentStatus 2 3.5528e+07 5.0608e+09 4062.6
## - EmployedYear 1 4.9172e+06 5.0302e+09 4063.2
## - SpecialProjectsCount 1 8.7142e+06 5.0340e+09 4063.4
## - Age 1 1.1782e+07 5.0371e+09 4063.6
## - DaysLateLast30 1 1.6643e+07 5.0419e+09 4063.8
## - PerfScoreID 1 4.0651e+07 5.0660e+09 4064.9
## - EmpSatisfaction 1 4.1738e+07 5.0670e+09 4064.9
## - EngagementSurvey 1 4.2335e+07 5.0676e+09 4064.9
## - Sex 1 4.2727e+07 5.0680e+09 4065.0
## <none> 5.0253e+09 4065.0
## - Absences 1 8.1211e+07 5.1065e+09 4066.7
## - HispanicLatino 1 1.2477e+08 5.1501e+09 4068.6
## - MaritalDesc 4 2.6945e+08 5.2947e+09 4069.0
## - Position 20 2.9791e+10 3.4817e+10 4468.3
##
## Step: AIC=4052.21
## Salary ~ MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## RecruitmentSource + Position + Age + CitizenDesc + RaceDesc +
## HispanicLatino + EmployedYear + ManagerName + EngagementSurvey +
## EmpSatisfaction + SpecialProjectsCount + DaysLateLast30 +
## Absences
##
## Df Sum of Sq RSS AIC
## - RecruitmentSource 7 1.9857e+08 6.1100e+09 4045.8
## - ManagerName 11 4.6206e+08 6.3735e+09 4047.4
## - EmploymentStatus 2 3.8123e+06 5.9153e+09 4048.4
## - RaceDesc 4 1.0949e+08 6.0210e+09 4048.4
## - CitizenDesc 2 4.0795e+07 5.9523e+09 4049.8
## - DaysLateLast30 1 3.9710e+03 5.9115e+09 4050.2
## - SpecialProjectsCount 1 1.1915e+06 5.9127e+09 4050.3
## - EmployedYear 1 2.3845e+06 5.9139e+09 4050.3
## - PerfScoreID 1 6.6387e+06 5.9181e+09 4050.5
## - Sex 1 1.1411e+07 5.9229e+09 4050.7
## - Age 1 1.4223e+07 5.9257e+09 4050.8
## - MaritalDesc 4 1.7298e+08 6.0844e+09 4050.8
## - EngagementSurvey 1 3.7358e+07 5.9488e+09 4051.7
## <none> 5.9115e+09 4052.2
## - EmpSatisfaction 1 1.0073e+08 6.0122e+09 4054.1
## - Absences 1 1.0282e+08 6.0143e+09 4054.2
## - HispanicLatino 1 1.0982e+08 6.0213e+09 4054.4
## - Position 21 3.7480e+10 4.3392e+10 4466.7
##
## Step: AIC=4045.78
## Salary ~ MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## Position + Age + CitizenDesc + RaceDesc + HispanicLatino +
## EmployedYear + ManagerName + EngagementSurvey + EmpSatisfaction +
## SpecialProjectsCount + DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - ManagerName 12 5.5242e+08 6.6625e+09 4041.6
## - RaceDesc 4 1.0747e+08 6.2175e+09 4041.8
## - EmploymentStatus 2 1.7785e+07 6.1278e+09 4042.4
## - DaysLateLast30 1 6.4880e+03 6.1100e+09 4043.8
## - EmployedYear 1 1.1265e+04 6.1101e+09 4043.8
## - SpecialProjectsCount 1 1.2223e+06 6.1113e+09 4043.8
## - PerfScoreID 1 1.5311e+06 6.1116e+09 4043.8
## - Sex 1 3.1824e+06 6.1132e+09 4043.9
## - CitizenDesc 2 5.7115e+07 6.1672e+09 4043.9
## - Age 1 2.2595e+07 6.1326e+09 4044.6
## - EngagementSurvey 1 2.3411e+07 6.1335e+09 4044.7
## - MaritalDesc 4 1.9048e+08 6.3005e+09 4044.8
## <none> 6.1100e+09 4045.8
## - EmpSatisfaction 1 7.9926e+07 6.1900e+09 4046.8
## - HispanicLatino 1 1.2183e+08 6.2319e+09 4048.3
## - Absences 1 1.3984e+08 6.2499e+09 4049.0
## - Position 22 5.0448e+10 5.6558e+10 4511.4
##
## Step: AIC=4041.6
## Salary ~ MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## Position + Age + CitizenDesc + RaceDesc + HispanicLatino +
## EmployedYear + EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - RaceDesc 4 6.9690e+07 6.7322e+09 4036.0
## - EmploymentStatus 2 3.5792e+07 6.6983e+09 4038.8
## - CitizenDesc 2 3.8285e+07 6.7007e+09 4038.9
## - MaritalDesc 4 1.6655e+08 6.8290e+09 4039.3
## - PerfScoreID 1 4.4511e+05 6.6629e+09 4039.6
## - DaysLateLast30 1 6.6983e+06 6.6692e+09 4039.8
## - Sex 1 1.4694e+07 6.6772e+09 4040.1
## - EmployedYear 1 1.5617e+07 6.6781e+09 4040.1
## - EngagementSurvey 1 2.9059e+07 6.6915e+09 4040.6
## - Age 1 3.8262e+07 6.7007e+09 4040.9
## - SpecialProjectsCount 1 5.8077e+07 6.7205e+09 4041.6
## <none> 6.6625e+09 4041.6
## - EmpSatisfaction 1 7.0380e+07 6.7328e+09 4042.0
## - HispanicLatino 1 7.4518e+07 6.7370e+09 4042.1
## - Absences 1 1.4853e+08 6.8110e+09 4044.7
## - Position 30 1.1205e+11 1.1871e+11 4641.2
##
## Step: AIC=4035.98
## Salary ~ MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## Position + Age + CitizenDesc + HispanicLatino + EmployedYear +
## EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - EmploymentStatus 2 3.1479e+07 6.7636e+09 4033.1
## - CitizenDesc 2 4.1903e+07 6.7741e+09 4033.4
## - MaritalDesc 4 1.6930e+08 6.9014e+09 4033.7
## - PerfScoreID 1 4.2204e+05 6.7326e+09 4034.0
## - DaysLateLast30 1 6.5068e+06 6.7387e+09 4034.2
## - EmployedYear 1 1.5317e+07 6.7475e+09 4034.5
## - Sex 1 1.9127e+07 6.7513e+09 4034.6
## - EngagementSurvey 1 2.8148e+07 6.7603e+09 4034.9
## - SpecialProjectsCount 1 4.7753e+07 6.7799e+09 4035.6
## - Age 1 5.5018e+07 6.7872e+09 4035.8
## <none> 6.7322e+09 4036.0
## - EmpSatisfaction 1 6.8492e+07 6.8006e+09 4036.3
## - HispanicLatino 1 7.9771e+07 6.8119e+09 4036.7
## - Absences 1 1.8490e+08 6.9171e+09 4040.2
## - Position 30 1.1253e+11 1.1926e+11 4634.2
##
## Step: AIC=4033.05
## Salary ~ MaritalDesc + Sex + PerfScoreID + Position + Age + CitizenDesc +
## HispanicLatino + EmployedYear + EngagementSurvey + EmpSatisfaction +
## SpecialProjectsCount + DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - CitizenDesc 2 3.6627e+07 6.8003e+09 4030.3
## - MaritalDesc 4 1.6518e+08 6.9288e+09 4030.6
## - PerfScoreID 1 7.0624e+05 6.7643e+09 4031.1
## - EmployedYear 1 8.3380e+05 6.7645e+09 4031.1
## - DaysLateLast30 1 7.6032e+06 6.7712e+09 4031.3
## - Sex 1 1.9473e+07 6.7831e+09 4031.7
## - EngagementSurvey 1 2.1823e+07 6.7855e+09 4031.8
## - Age 1 4.8383e+07 6.8120e+09 4032.7
## - SpecialProjectsCount 1 5.8088e+07 6.8217e+09 4033.0
## <none> 6.7636e+09 4033.1
## - EmpSatisfaction 1 6.9826e+07 6.8335e+09 4033.4
## - HispanicLatino 1 8.2848e+07 6.8465e+09 4033.8
## - Absences 1 1.7912e+08 6.9428e+09 4037.0
## - Position 30 1.1261e+11 1.1937e+11 4630.4
##
## Step: AIC=4030.29
## Salary ~ MaritalDesc + Sex + PerfScoreID + Position + Age + HispanicLatino +
## EmployedYear + EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - MaritalDesc 4 1.5639e+08 6.9566e+09 4027.5
## - PerfScoreID 1 2.1766e+05 6.8005e+09 4028.3
## - EmployedYear 1 8.4629e+05 6.8011e+09 4028.3
## - DaysLateLast30 1 4.1764e+06 6.8044e+09 4028.4
## - EngagementSurvey 1 1.5689e+07 6.8159e+09 4028.8
## - Sex 1 2.3692e+07 6.8239e+09 4029.1
## - Age 1 5.0660e+07 6.8509e+09 4030.0
## - SpecialProjectsCount 1 5.1421e+07 6.8517e+09 4030.0
## <none> 6.8003e+09 4030.3
## - EmpSatisfaction 1 8.6299e+07 6.8866e+09 4031.2
## - HispanicLatino 1 8.6596e+07 6.8869e+09 4031.2
## - Absences 1 1.7909e+08 6.9794e+09 4034.2
## - Position 30 1.1294e+11 1.1974e+11 4627.1
##
## Step: AIC=4027.5
## Salary ~ Sex + PerfScoreID + Position + Age + HispanicLatino +
## EmployedYear + EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - PerfScoreID 1 5.7022e+05 6.9572e+09 4025.5
## - EmployedYear 1 2.1762e+06 6.9588e+09 4025.6
## - DaysLateLast30 1 4.7459e+06 6.9614e+09 4025.7
## - Sex 1 2.1569e+07 6.9782e+09 4026.2
## - EngagementSurvey 1 2.4490e+07 6.9811e+09 4026.3
## - SpecialProjectsCount 1 4.7463e+07 7.0041e+09 4027.1
## - Age 1 5.8618e+07 7.0153e+09 4027.4
## <none> 6.9566e+09 4027.5
## - HispanicLatino 1 8.4798e+07 7.0414e+09 4028.3
## - EmpSatisfaction 1 9.8047e+07 7.0547e+09 4028.7
## - Absences 1 1.5980e+08 7.1164e+09 4030.7
## - Position 30 1.1313e+11 1.2008e+11 4619.8
##
## Step: AIC=4025.52
## Salary ~ Sex + Position + Age + HispanicLatino + EmployedYear +
## EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - EmployedYear 1 2.1076e+06 6.9593e+09 4023.6
## - DaysLateLast30 1 4.5937e+06 6.9618e+09 4023.7
## - Sex 1 2.1836e+07 6.9791e+09 4024.2
## - EngagementSurvey 1 2.7485e+07 6.9847e+09 4024.4
## - SpecialProjectsCount 1 4.6900e+07 7.0041e+09 4025.1
## <none> 6.9572e+09 4025.5
## - Age 1 6.1310e+07 7.0185e+09 4025.5
## - HispanicLatino 1 8.5141e+07 7.0424e+09 4026.3
## - EmpSatisfaction 1 1.0379e+08 7.0610e+09 4026.9
## - Absences 1 1.6309e+08 7.1203e+09 4028.8
## - Position 30 1.1455e+11 1.2151e+11 4620.5
##
## Step: AIC=4023.58
## Salary ~ Sex + Position + Age + HispanicLatino + EngagementSurvey +
## EmpSatisfaction + SpecialProjectsCount + DaysLateLast30 +
## Absences
##
## Df Sum of Sq RSS AIC
## - DaysLateLast30 1 5.6303e+06 6.9650e+09 4021.8
## - Sex 1 2.1251e+07 6.9806e+09 4022.3
## - EngagementSurvey 1 2.7238e+07 6.9866e+09 4022.5
## - SpecialProjectsCount 1 4.5330e+07 7.0047e+09 4023.1
## <none> 6.9593e+09 4023.6
## - Age 1 6.2261e+07 7.0216e+09 4023.6
## - HispanicLatino 1 8.4314e+07 7.0436e+09 4024.3
## - EmpSatisfaction 1 1.0798e+08 7.0673e+09 4025.1
## - Absences 1 1.6446e+08 7.1238e+09 4026.9
## - Position 30 1.1534e+11 1.2229e+11 4620.0
##
## Step: AIC=4021.77
## Salary ~ Sex + Position + Age + HispanicLatino + EngagementSurvey +
## EmpSatisfaction + SpecialProjectsCount + Absences
##
## Df Sum of Sq RSS AIC
## - Sex 1 1.9188e+07 6.9841e+09 4020.4
## - EngagementSurvey 1 2.2576e+07 6.9875e+09 4020.5
## - SpecialProjectsCount 1 4.6881e+07 7.0118e+09 4021.3
## <none> 6.9650e+09 4021.8
## - Age 1 6.2528e+07 7.0275e+09 4021.8
## - HispanicLatino 1 8.3523e+07 7.0485e+09 4022.5
## - EmpSatisfaction 1 1.0325e+08 7.0682e+09 4023.1
## - Absences 1 1.6379e+08 7.1287e+09 4025.1
## - Position 30 1.1535e+11 1.2231e+11 4618.0
##
## Step: AIC=4020.4
## Salary ~ Position + Age + HispanicLatino + EngagementSurvey +
## EmpSatisfaction + SpecialProjectsCount + Absences
##
## Df Sum of Sq RSS AIC
## - EngagementSurvey 1 2.2263e+07 7.0064e+09 4019.1
## - SpecialProjectsCount 1 5.3913e+07 7.0381e+09 4020.2
## <none> 6.9841e+09 4020.4
## - Age 1 6.2695e+07 7.0468e+09 4020.4
## - HispanicLatino 1 8.8914e+07 7.0731e+09 4021.3
## - EmpSatisfaction 1 1.0812e+08 7.0923e+09 4021.9
## - Absences 1 1.6174e+08 7.1459e+09 4023.6
## - Position 30 1.1534e+11 1.2232e+11 4616.0
##
## Step: AIC=4019.13
## Salary ~ Position + Age + HispanicLatino + EmpSatisfaction +
## SpecialProjectsCount + Absences
##
## Df Sum of Sq RSS AIC
## - SpecialProjectsCount 1 5.0003e+07 7.0564e+09 4018.8
## <none> 7.0064e+09 4019.1
## - Age 1 6.7350e+07 7.0738e+09 4019.3
## - HispanicLatino 1 8.4136e+07 7.0905e+09 4019.9
## - EmpSatisfaction 1 1.3006e+08 7.1365e+09 4021.3
## - Absences 1 1.6060e+08 7.1670e+09 4022.3
## - Position 30 1.1573e+11 1.2273e+11 4614.8
##
## Step: AIC=4018.76
## Salary ~ Position + Age + HispanicLatino + EmpSatisfaction +
## Absences
##
## Df Sum of Sq RSS AIC
## <none> 7.0564e+09 4018.8
## - Age 1 6.1906e+07 7.1183e+09 4018.8
## - HispanicLatino 1 8.3894e+07 7.1403e+09 4019.5
## - EmpSatisfaction 1 1.3295e+08 7.1894e+09 4021.0
## - Absences 1 1.6549e+08 7.2219e+09 4022.1
## - Position 30 1.5785e+11 1.6491e+11 4680.4##
## Call:
## lm(formula = Salary ~ Position + Age + HispanicLatino + EmpSatisfaction +
## Absences, data = HRtrain)
##
## Coefficients:
## (Intercept) PositionAdministrative Assistant
## 56692.0 -10822.8
## PositionArea Sales Manager PositionBI Developer
## 476.7 30888.2
## PositionBI Director PositionCIO
## 45476.4 154225.8
## PositionData Analyst PositionData Architect
## 25968.5 85182.1
## PositionDatabase Administrator PositionDirector of Operations
## 48230.7 104691.6
## PositionDirector of Sales PositionEnterprise Architect
## 113327.4 38558.9
## PositionIT Director PositionIT Manager - DB
## 111995.1 79325.4
## PositionIT Manager - Infra PositionIT Manager - Support
## 95948.0 73919.4
## PositionIT Support PositionNetwork Engineer
## 1531.5 -2222.9
## PositionPresident & CEO PositionPrincipal Data Architect
## 187313.4 58250.3
## PositionProduction Manager PositionProduction Technician I
## 13372.2 -8509.7
## PositionProduction Technician II PositionSales Manager
## 878.4 3950.9
## PositionSenior BI Developer PositionShared Services Manager
## 22682.1 27398.6
## PositionSoftware Engineer PositionSoftware Engineering Manager
## 30992.0 14189.9
## PositionSr. Accountant PositionSr. DBA
## 40115.1 39525.6
## PositionSr. Network Engineer Age
## 27823.6 65.5
## HispanicLatinoYes EmpSatisfaction
## -2523.9 864.3
## Absences
## 153.8#Both direction, Stepwise Regression
step(full_model,scope = list(lower=reduced_model,upper=full_model),direction="both")## Start: AIC=4065.02
## Salary ~ MarriedID + MaritalDesc + Sex + EmploymentStatus + Department +
## PerfScoreID + RecruitmentSource + Position + State + Age +
## CitizenDesc + RaceDesc + HispanicLatino + EmployedYear +
## ManagerName + EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
##
## Step: AIC=4065.02
## Salary ~ MarriedID + MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## RecruitmentSource + Position + State + Age + CitizenDesc +
## RaceDesc + HispanicLatino + EmployedYear + ManagerName +
## EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
##
## Step: AIC=4065.02
## Salary ~ MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## RecruitmentSource + Position + State + Age + CitizenDesc +
## RaceDesc + HispanicLatino + EmployedYear + ManagerName +
## EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - State 25 8.8616e+08 5.9115e+09 4052.2
## - RecruitmentSource 7 1.5335e+08 5.1787e+09 4057.9
## - CitizenDesc 2 2.5962e+07 5.0513e+09 4062.2
## - RaceDesc 4 1.2120e+08 5.1465e+09 4062.5
## - ManagerName 10 3.9915e+08 5.4244e+09 4062.5
## - EmploymentStatus 2 3.5528e+07 5.0608e+09 4062.6
## - EmployedYear 1 4.9172e+06 5.0302e+09 4063.2
## - SpecialProjectsCount 1 8.7142e+06 5.0340e+09 4063.4
## - Age 1 1.1782e+07 5.0371e+09 4063.6
## - DaysLateLast30 1 1.6643e+07 5.0419e+09 4063.8
## - PerfScoreID 1 4.0651e+07 5.0660e+09 4064.9
## - EmpSatisfaction 1 4.1738e+07 5.0670e+09 4064.9
## - EngagementSurvey 1 4.2335e+07 5.0676e+09 4064.9
## - Sex 1 4.2727e+07 5.0680e+09 4065.0
## <none> 5.0253e+09 4065.0
## - Absences 1 8.1211e+07 5.1065e+09 4066.7
## - HispanicLatino 1 1.2477e+08 5.1501e+09 4068.6
## - MaritalDesc 4 2.6945e+08 5.2947e+09 4069.0
## - Position 20 2.9791e+10 3.4817e+10 4468.3
##
## Step: AIC=4052.21
## Salary ~ MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## RecruitmentSource + Position + Age + CitizenDesc + RaceDesc +
## HispanicLatino + EmployedYear + ManagerName + EngagementSurvey +
## EmpSatisfaction + SpecialProjectsCount + DaysLateLast30 +
## Absences
##
## Df Sum of Sq RSS AIC
## - RecruitmentSource 7 1.9857e+08 6.1100e+09 4045.8
## - ManagerName 11 4.6206e+08 6.3735e+09 4047.4
## - EmploymentStatus 2 3.8123e+06 5.9153e+09 4048.4
## - RaceDesc 4 1.0949e+08 6.0210e+09 4048.4
## - CitizenDesc 2 4.0795e+07 5.9523e+09 4049.8
## - DaysLateLast30 1 3.9710e+03 5.9115e+09 4050.2
## - SpecialProjectsCount 1 1.1915e+06 5.9127e+09 4050.3
## - EmployedYear 1 2.3845e+06 5.9139e+09 4050.3
## - PerfScoreID 1 6.6387e+06 5.9181e+09 4050.5
## - Sex 1 1.1411e+07 5.9229e+09 4050.7
## - Age 1 1.4223e+07 5.9257e+09 4050.8
## - MaritalDesc 4 1.7298e+08 6.0844e+09 4050.8
## - EngagementSurvey 1 3.7358e+07 5.9488e+09 4051.7
## <none> 5.9115e+09 4052.2
## - EmpSatisfaction 1 1.0073e+08 6.0122e+09 4054.1
## - Absences 1 1.0282e+08 6.0143e+09 4054.2
## - HispanicLatino 1 1.0982e+08 6.0213e+09 4054.4
## + State 25 8.8616e+08 5.0253e+09 4065.0
## - Position 21 3.7480e+10 4.3392e+10 4466.7
##
## Step: AIC=4045.78
## Salary ~ MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## Position + Age + CitizenDesc + RaceDesc + HispanicLatino +
## EmployedYear + ManagerName + EngagementSurvey + EmpSatisfaction +
## SpecialProjectsCount + DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - ManagerName 12 5.5242e+08 6.6625e+09 4041.6
## - RaceDesc 4 1.0747e+08 6.2175e+09 4041.8
## - EmploymentStatus 2 1.7785e+07 6.1278e+09 4042.4
## - DaysLateLast30 1 6.4880e+03 6.1100e+09 4043.8
## - EmployedYear 1 1.1265e+04 6.1101e+09 4043.8
## - SpecialProjectsCount 1 1.2223e+06 6.1113e+09 4043.8
## - PerfScoreID 1 1.5311e+06 6.1116e+09 4043.8
## - Sex 1 3.1824e+06 6.1132e+09 4043.9
## - CitizenDesc 2 5.7115e+07 6.1672e+09 4043.9
## - Age 1 2.2595e+07 6.1326e+09 4044.6
## - EngagementSurvey 1 2.3411e+07 6.1335e+09 4044.7
## - MaritalDesc 4 1.9048e+08 6.3005e+09 4044.8
## <none> 6.1100e+09 4045.8
## - EmpSatisfaction 1 7.9926e+07 6.1900e+09 4046.8
## - HispanicLatino 1 1.2183e+08 6.2319e+09 4048.3
## - Absences 1 1.3984e+08 6.2499e+09 4049.0
## + RecruitmentSource 7 1.9857e+08 5.9115e+09 4052.2
## + State 25 9.3139e+08 5.1787e+09 4057.9
## - Position 22 5.0448e+10 5.6558e+10 4511.4
##
## Step: AIC=4041.6
## Salary ~ MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## Position + Age + CitizenDesc + RaceDesc + HispanicLatino +
## EmployedYear + EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - RaceDesc 4 6.9690e+07 6.7322e+09 4036.0
## - EmploymentStatus 2 3.5792e+07 6.6983e+09 4038.8
## - CitizenDesc 2 3.8285e+07 6.7007e+09 4038.9
## - MaritalDesc 4 1.6655e+08 6.8290e+09 4039.3
## - PerfScoreID 1 4.4511e+05 6.6629e+09 4039.6
## - DaysLateLast30 1 6.6983e+06 6.6692e+09 4039.8
## - Sex 1 1.4694e+07 6.6772e+09 4040.1
## - EmployedYear 1 1.5617e+07 6.6781e+09 4040.1
## - EngagementSurvey 1 2.9059e+07 6.6915e+09 4040.6
## - Age 1 3.8262e+07 6.7007e+09 4040.9
## - SpecialProjectsCount 1 5.8077e+07 6.7205e+09 4041.6
## <none> 6.6625e+09 4041.6
## - EmpSatisfaction 1 7.0380e+07 6.7328e+09 4042.0
## - HispanicLatino 1 7.4518e+07 6.7370e+09 4042.1
## - Absences 1 1.4853e+08 6.8110e+09 4044.7
## + ManagerName 12 5.5242e+08 6.1100e+09 4045.8
## + RecruitmentSource 8 2.8894e+08 6.3735e+09 4047.4
## + State 26 9.8737e+08 5.6751e+09 4056.9
## - Position 30 1.1205e+11 1.1871e+11 4641.2
##
## Step: AIC=4035.98
## Salary ~ MaritalDesc + Sex + EmploymentStatus + PerfScoreID +
## Position + Age + CitizenDesc + HispanicLatino + EmployedYear +
## EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - EmploymentStatus 2 3.1479e+07 6.7636e+09 4033.1
## - CitizenDesc 2 4.1903e+07 6.7741e+09 4033.4
## - MaritalDesc 4 1.6930e+08 6.9014e+09 4033.7
## - PerfScoreID 1 4.2204e+05 6.7326e+09 4034.0
## - DaysLateLast30 1 6.5068e+06 6.7387e+09 4034.2
## - EmployedYear 1 1.5317e+07 6.7475e+09 4034.5
## - Sex 1 1.9127e+07 6.7513e+09 4034.6
## - EngagementSurvey 1 2.8148e+07 6.7603e+09 4034.9
## - SpecialProjectsCount 1 4.7753e+07 6.7799e+09 4035.6
## - Age 1 5.5018e+07 6.7872e+09 4035.8
## <none> 6.7322e+09 4036.0
## - EmpSatisfaction 1 6.8492e+07 6.8006e+09 4036.3
## - HispanicLatino 1 7.9771e+07 6.8119e+09 4036.7
## - Absences 1 1.8490e+08 6.9171e+09 4040.2
## + RaceDesc 4 6.9690e+07 6.6625e+09 4041.6
## + ManagerName 12 5.1464e+08 6.2175e+09 4041.8
## + RecruitmentSource 8 2.6540e+08 6.4668e+09 4042.8
## + State 26 9.3855e+08 5.7936e+09 4053.6
## - Position 30 1.1253e+11 1.1926e+11 4634.2
##
## Step: AIC=4033.05
## Salary ~ MaritalDesc + Sex + PerfScoreID + Position + Age + CitizenDesc +
## HispanicLatino + EmployedYear + EngagementSurvey + EmpSatisfaction +
## SpecialProjectsCount + DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - CitizenDesc 2 3.6627e+07 6.8003e+09 4030.3
## - MaritalDesc 4 1.6518e+08 6.9288e+09 4030.6
## - PerfScoreID 1 7.0624e+05 6.7643e+09 4031.1
## - EmployedYear 1 8.3380e+05 6.7645e+09 4031.1
## - DaysLateLast30 1 7.6032e+06 6.7712e+09 4031.3
## - Sex 1 1.9473e+07 6.7831e+09 4031.7
## - EngagementSurvey 1 2.1823e+07 6.7855e+09 4031.8
## - Age 1 4.8383e+07 6.8120e+09 4032.7
## - SpecialProjectsCount 1 5.8088e+07 6.8217e+09 4033.0
## <none> 6.7636e+09 4033.1
## - EmpSatisfaction 1 6.9826e+07 6.8335e+09 4033.4
## - HispanicLatino 1 8.2848e+07 6.8465e+09 4033.8
## + EmploymentStatus 2 3.1479e+07 6.7322e+09 4036.0
## - Absences 1 1.7912e+08 6.9428e+09 4037.0
## + ManagerName 12 5.3080e+08 6.2328e+09 4038.3
## + RaceDesc 4 6.5377e+07 6.6983e+09 4038.8
## + RecruitmentSource 8 2.9307e+08 6.4706e+09 4038.9
## + State 26 9.1195e+08 5.8517e+09 4051.9
## - Position 30 1.1261e+11 1.1937e+11 4630.4
##
## Step: AIC=4030.29
## Salary ~ MaritalDesc + Sex + PerfScoreID + Position + Age + HispanicLatino +
## EmployedYear + EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - MaritalDesc 4 1.5639e+08 6.9566e+09 4027.5
## - PerfScoreID 1 2.1766e+05 6.8005e+09 4028.3
## - EmployedYear 1 8.4629e+05 6.8011e+09 4028.3
## - DaysLateLast30 1 4.1764e+06 6.8044e+09 4028.4
## - EngagementSurvey 1 1.5689e+07 6.8159e+09 4028.8
## - Sex 1 2.3692e+07 6.8239e+09 4029.1
## - Age 1 5.0660e+07 6.8509e+09 4030.0
## - SpecialProjectsCount 1 5.1421e+07 6.8517e+09 4030.0
## <none> 6.8003e+09 4030.3
## - EmpSatisfaction 1 8.6299e+07 6.8866e+09 4031.2
## - HispanicLatino 1 8.6596e+07 6.8869e+09 4031.2
## + CitizenDesc 2 3.6627e+07 6.7636e+09 4033.1
## + EmploymentStatus 2 2.6203e+07 6.7741e+09 4033.4
## - Absences 1 1.7909e+08 6.9794e+09 4034.2
## + RaceDesc 4 6.9110e+07 6.7311e+09 4036.0
## + RecruitmentSource 8 3.0015e+08 6.5001e+09 4036.0
## + ManagerName 12 5.0609e+08 6.2942e+09 4036.6
## + State 26 9.1229e+08 5.8880e+09 4049.3
## - Position 30 1.1294e+11 1.1974e+11 4627.1
##
## Step: AIC=4027.5
## Salary ~ Sex + PerfScoreID + Position + Age + HispanicLatino +
## EmployedYear + EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - PerfScoreID 1 5.7022e+05 6.9572e+09 4025.5
## - EmployedYear 1 2.1762e+06 6.9588e+09 4025.6
## - DaysLateLast30 1 4.7459e+06 6.9614e+09 4025.7
## - Sex 1 2.1569e+07 6.9782e+09 4026.2
## - EngagementSurvey 1 2.4490e+07 6.9811e+09 4026.3
## + MarriedID 1 9.4084e+07 6.8626e+09 4026.4
## - SpecialProjectsCount 1 4.7463e+07 7.0041e+09 4027.1
## - Age 1 5.8618e+07 7.0153e+09 4027.4
## <none> 6.9566e+09 4027.5
## - HispanicLatino 1 8.4798e+07 7.0414e+09 4028.3
## - EmpSatisfaction 1 9.8047e+07 7.0547e+09 4028.7
## + MaritalDesc 4 1.5639e+08 6.8003e+09 4030.3
## + CitizenDesc 2 2.7833e+07 6.9288e+09 4030.6
## - Absences 1 1.5980e+08 7.1164e+09 4030.7
## + EmploymentStatus 2 2.4114e+07 6.9325e+09 4030.7
## + RaceDesc 4 7.2125e+07 6.8845e+09 4033.1
## + RecruitmentSource 8 3.0732e+08 6.6493e+09 4033.1
## + ManagerName 12 4.8438e+08 6.4723e+09 4035.0
## + State 26 8.3896e+08 6.1177e+09 4050.1
## - Position 30 1.1313e+11 1.2008e+11 4619.8
##
## Step: AIC=4025.52
## Salary ~ Sex + Position + Age + HispanicLatino + EmployedYear +
## EngagementSurvey + EmpSatisfaction + SpecialProjectsCount +
## DaysLateLast30 + Absences
##
## Df Sum of Sq RSS AIC
## - EmployedYear 1 2.1076e+06 6.9593e+09 4023.6
## - DaysLateLast30 1 4.5937e+06 6.9618e+09 4023.7
## - Sex 1 2.1836e+07 6.9791e+09 4024.2
## + MarriedID 1 9.4168e+07 6.8630e+09 4024.4
## - EngagementSurvey 1 2.7485e+07 6.9847e+09 4024.4
## - SpecialProjectsCount 1 4.6900e+07 7.0041e+09 4025.1
## <none> 6.9572e+09 4025.5
## - Age 1 6.1310e+07 7.0185e+09 4025.5
## - HispanicLatino 1 8.5141e+07 7.0424e+09 4026.3
## - EmpSatisfaction 1 1.0379e+08 7.0610e+09 4026.9
## + PerfScoreID 1 5.7022e+05 6.9566e+09 4027.5
## + MaritalDesc 4 1.5674e+08 6.8005e+09 4028.3
## + CitizenDesc 2 2.7465e+07 6.9298e+09 4028.6
## + EmploymentStatus 2 2.4476e+07 6.9327e+09 4028.7
## - Absences 1 1.6309e+08 7.1203e+09 4028.8
## + RaceDesc 4 7.2136e+07 6.8851e+09 4031.1
## + RecruitmentSource 8 3.0545e+08 6.6518e+09 4031.2
## + ManagerName 12 4.8452e+08 6.4727e+09 4033.0
## + State 26 8.3337e+08 6.1239e+09 4048.3
## - Position 30 1.1455e+11 1.2151e+11 4620.5
##
## Step: AIC=4023.58
## Salary ~ Sex + Position + Age + HispanicLatino + EngagementSurvey +
## EmpSatisfaction + SpecialProjectsCount + DaysLateLast30 +
## Absences
##
## Df Sum of Sq RSS AIC
## - DaysLateLast30 1 5.6303e+06 6.9650e+09 4021.8
## - Sex 1 2.1251e+07 6.9806e+09 4022.3
## - EngagementSurvey 1 2.7238e+07 6.9866e+09 4022.5
## + MarriedID 1 9.0833e+07 6.8685e+09 4022.6
## - SpecialProjectsCount 1 4.5330e+07 7.0047e+09 4023.1
## <none> 6.9593e+09 4023.6
## - Age 1 6.2261e+07 7.0216e+09 4023.6
## - HispanicLatino 1 8.4314e+07 7.0436e+09 4024.3
## - EmpSatisfaction 1 1.0798e+08 7.0673e+09 4025.1
## + EmployedYear 1 2.1076e+06 6.9572e+09 4025.5
## + PerfScoreID 1 5.0159e+05 6.9588e+09 4025.6
## + MaritalDesc 4 1.5804e+08 6.8013e+09 4026.3
## + CitizenDesc 2 2.7668e+07 6.9317e+09 4026.7
## - Absences 1 1.6446e+08 7.1238e+09 4026.9
## + EmploymentStatus 2 1.3806e+07 6.9455e+09 4027.1
## + RaceDesc 4 7.2227e+07 6.8871e+09 4029.2
## + RecruitmentSource 8 3.0412e+08 6.6552e+09 4029.4
## + ManagerName 12 4.8576e+08 6.4736e+09 4031.0
## + State 26 8.3500e+08 6.1243e+09 4046.3
## - Position 30 1.1534e+11 1.2229e+11 4620.0
##
## Step: AIC=4021.77
## Salary ~ Sex + Position + Age + HispanicLatino + EngagementSurvey +
## EmpSatisfaction + SpecialProjectsCount + Absences
##
## Df Sum of Sq RSS AIC
## - Sex 1 1.9188e+07 6.9841e+09 4020.4
## - EngagementSurvey 1 2.2576e+07 6.9875e+09 4020.5
## + MarriedID 1 9.4842e+07 6.8701e+09 4020.6
## - SpecialProjectsCount 1 4.6881e+07 7.0118e+09 4021.3
## <none> 6.9650e+09 4021.8
## - Age 1 6.2528e+07 7.0275e+09 4021.8
## - HispanicLatino 1 8.3523e+07 7.0485e+09 4022.5
## - EmpSatisfaction 1 1.0325e+08 7.0682e+09 4023.1
## + DaysLateLast30 1 5.6303e+06 6.9593e+09 4023.6
## + EmployedYear 1 3.1442e+06 6.9618e+09 4023.7
## + PerfScoreID 1 7.0046e+05 6.9643e+09 4023.7
## + MaritalDesc 4 1.5807e+08 6.8069e+09 4024.5
## + CitizenDesc 2 2.5034e+07 6.9399e+09 4024.9
## - Absences 1 1.6379e+08 7.1287e+09 4025.1
## + EmploymentStatus 2 1.6498e+07 6.9485e+09 4025.2
## + RaceDesc 4 7.1627e+07 6.8933e+09 4027.4
## + RecruitmentSource 8 2.9240e+08 6.6726e+09 4027.9
## + ManagerName 12 4.9103e+08 6.4739e+09 4029.0
## + State 26 8.3975e+08 6.1252e+09 4044.3
## - Position 30 1.1535e+11 1.2231e+11 4618.0
##
## Step: AIC=4020.4
## Salary ~ Position + Age + HispanicLatino + EngagementSurvey +
## EmpSatisfaction + SpecialProjectsCount + Absences
##
## Df Sum of Sq RSS AIC
## - EngagementSurvey 1 2.2263e+07 7.0064e+09 4019.1
## + MarriedID 1 9.2304e+07 6.8918e+09 4019.4
## - SpecialProjectsCount 1 5.3913e+07 7.0381e+09 4020.2
## <none> 6.9841e+09 4020.4
## - Age 1 6.2695e+07 7.0468e+09 4020.4
## - HispanicLatino 1 8.8914e+07 7.0731e+09 4021.3
## + Sex 1 1.9188e+07 6.9650e+09 4021.8
## - EmpSatisfaction 1 1.0812e+08 7.0923e+09 4021.9
## + DaysLateLast30 1 3.5670e+06 6.9806e+09 4022.3
## + EmployedYear 1 2.2452e+06 6.9819e+09 4022.3
## + PerfScoreID 1 1.8256e+05 6.9840e+09 4022.4
## + MaritalDesc 4 1.5577e+08 6.8284e+09 4023.2
## + CitizenDesc 2 2.8435e+07 6.9557e+09 4023.5
## - Absences 1 1.6174e+08 7.1459e+09 4023.6
## + EmploymentStatus 2 1.6333e+07 6.9678e+09 4023.9
## + RaceDesc 4 7.7145e+07 6.9070e+09 4025.9
## + RecruitmentSource 8 2.7487e+08 6.7093e+09 4027.2
## + ManagerName 12 5.0187e+08 6.4823e+09 4027.3
## + State 26 8.0862e+08 6.1755e+09 4044.2
## - Position 30 1.1534e+11 1.2232e+11 4616.0
##
## Step: AIC=4019.13
## Salary ~ Position + Age + HispanicLatino + EmpSatisfaction +
## SpecialProjectsCount + Absences
##
## Df Sum of Sq RSS AIC
## + MarriedID 1 1.0009e+08 6.9063e+09 4017.8
## - SpecialProjectsCount 1 5.0003e+07 7.0564e+09 4018.8
## <none> 7.0064e+09 4019.1
## - Age 1 6.7350e+07 7.0738e+09 4019.3
## - HispanicLatino 1 8.4136e+07 7.0905e+09 4019.9
## + EngagementSurvey 1 2.2263e+07 6.9841e+09 4020.4
## + Sex 1 1.8874e+07 6.9875e+09 4020.5
## + PerfScoreID 1 5.2234e+06 7.0012e+09 4021.0
## + DaysLateLast30 1 1.7791e+06 7.0046e+09 4021.1
## + EmployedYear 1 7.8920e+05 7.0056e+09 4021.1
## - EmpSatisfaction 1 1.3006e+08 7.1365e+09 4021.3
## + MaritalDesc 4 1.6653e+08 6.8399e+09 4021.6
## - Absences 1 1.6060e+08 7.1670e+09 4022.3
## + CitizenDesc 2 2.2691e+07 6.9837e+09 4022.4
## + EmploymentStatus 2 1.1007e+07 6.9954e+09 4022.8
## + RaceDesc 4 7.4920e+07 6.9315e+09 4024.7
## + ManagerName 12 4.9688e+08 6.5095e+09 4026.3
## + RecruitmentSource 8 2.6492e+08 6.7415e+09 4026.3
## + State 26 8.0689e+08 6.1995e+09 4043.1
## - Position 30 1.1573e+11 1.2273e+11 4614.8
##
## Step: AIC=4017.83
## Salary ~ Position + Age + HispanicLatino + EmpSatisfaction +
## SpecialProjectsCount + Absences + MarriedID
##
## Df Sum of Sq RSS AIC
## - Age 1 5.8649e+07 6.9650e+09 4017.8
## <none> 6.9063e+09 4017.8
## - SpecialProjectsCount 1 6.1873e+07 6.9682e+09 4017.9
## + Sex 1 2.1552e+07 6.8848e+09 4019.1
## - MarriedID 1 1.0009e+08 7.0064e+09 4019.1
## + EngagementSurvey 1 1.4480e+07 6.8918e+09 4019.4
## - HispanicLatino 1 1.1333e+08 7.0197e+09 4019.6
## - EmpSatisfaction 1 1.1518e+08 7.0215e+09 4019.6
## + PerfScoreID 1 5.2146e+06 6.9011e+09 4019.7
## + EmployedYear 1 2.9652e+06 6.9034e+09 4019.7
## + DaysLateLast30 1 2.8856e+06 6.9034e+09 4019.7
## + CitizenDesc 2 2.7332e+07 6.8790e+09 4020.9
## + EmploymentStatus 2 8.6172e+06 6.8977e+09 4021.5
## + MaritalDesc 3 6.6443e+07 6.8399e+09 4021.6
## - Absences 1 1.9031e+08 7.0966e+09 4022.1
## + RaceDesc 4 7.8459e+07 6.8279e+09 4023.2
## + ManagerName 12 5.2205e+08 6.3843e+09 4023.8
## + RecruitmentSource 8 2.7560e+08 6.6307e+09 4024.5
## + State 26 8.4082e+08 6.0655e+09 4040.1
## - Position 30 1.1583e+11 1.2273e+11 4616.8
##
## Step: AIC=4017.77
## Salary ~ Position + HispanicLatino + EmpSatisfaction + SpecialProjectsCount +
## Absences + MarriedID
##
## Df Sum of Sq RSS AIC
## - SpecialProjectsCount 1 5.6727e+07 7.0217e+09 4017.6
## <none> 6.9650e+09 4017.8
## + Age 1 5.8649e+07 6.9063e+09 4017.8
## + Sex 1 2.1805e+07 6.9432e+09 4019.1
## + EngagementSurvey 1 1.7715e+07 6.9473e+09 4019.2
## - EmpSatisfaction 1 1.0728e+08 7.0722e+09 4019.3
## - MarriedID 1 1.0879e+08 7.0738e+09 4019.3
## + PerfScoreID 1 1.0073e+07 6.9549e+09 4019.4
## + EmployedYear 1 3.9580e+06 6.9610e+09 4019.6
## + DaysLateLast30 1 3.7645e+06 6.9612e+09 4019.6
## - HispanicLatino 1 1.2791e+08 7.0929e+09 4019.9
## + CitizenDesc 2 2.7385e+07 6.9376e+09 4020.9
## + MaritalDesc 3 6.9143e+07 6.8958e+09 4021.5
## + EmploymentStatus 2 7.7289e+06 6.9572e+09 4021.5
## - Absences 1 1.7913e+08 7.1441e+09 4021.6
## + RaceDesc 4 9.5866e+07 6.8691e+09 4022.6
## + ManagerName 12 5.4704e+08 6.4179e+09 4023.0
## + RecruitmentSource 8 2.8206e+08 6.6829e+09 4024.3
## + State 26 8.4867e+08 6.1163e+09 4040.0
## - Position 30 1.2238e+11 1.2935e+11 4626.8
##
## Step: AIC=4017.63
## Salary ~ Position + HispanicLatino + EmpSatisfaction + Absences +
## MarriedID
##
## Df Sum of Sq RSS AIC
## <none> 7.0217e+09 4017.6
## + SpecialProjectsCount 1 5.6727e+07 6.9650e+09 4017.8
## + Age 1 5.3503e+07 6.9682e+09 4017.9
## + Sex 1 2.9301e+07 6.9924e+09 4018.7
## - MarriedID 1 9.6619e+07 7.1183e+09 4018.8
## + EngagementSurvey 1 1.4349e+07 7.0073e+09 4019.2
## - EmpSatisfaction 1 1.1130e+08 7.1330e+09 4019.2
## + PerfScoreID 1 5.2342e+06 7.0165e+09 4019.5
## + DaysLateLast30 1 2.0351e+06 7.0197e+09 4019.6
## + EmployedYear 1 1.4092e+06 7.0203e+09 4019.6
## - HispanicLatino 1 1.2481e+08 7.1465e+09 4019.7
## + CitizenDesc 2 1.9603e+07 7.0021e+09 4021.0
## + ManagerName 12 6.0158e+08 6.4201e+09 4021.1
## + MaritalDesc 3 7.1657e+07 6.9500e+09 4021.3
## + EmploymentStatus 2 1.0361e+07 7.0113e+09 4021.3
## - Absences 1 1.8300e+08 7.2047e+09 4021.5
## + RaceDesc 4 8.2289e+07 6.9394e+09 4022.9
## + RecruitmentSource 8 3.0288e+08 6.7188e+09 4023.5
## + State 26 8.6493e+08 6.1568e+09 4039.5
## - Position 30 1.6379e+11 1.7081e+11 4688.5##
## Call:
## lm(formula = Salary ~ Position + HispanicLatino + EmpSatisfaction +
## Absences + MarriedID, data = HRtrain)
##
## Coefficients:
## (Intercept) PositionAdministrative Assistant
## 59675.3 -10431.0
## PositionArea Sales Manager PositionBI Developer
## 1054.3 31874.0
## PositionBI Director PositionCIO
## 47432.1 154215.8
## PositionData Analyst PositionData Architect
## 26402.4 85475.7
## PositionDatabase Administrator PositionDirector of Operations
## 49005.6 104428.4
## PositionDirector of Sales PositionEnterprise Architect
## 115505.2 39656.0
## PositionIT Director PositionIT Manager - DB
## 111928.4 80015.5
## PositionIT Manager - Infra PositionIT Manager - Support
## 97379.6 74605.6
## PositionIT Support PositionNetwork Engineer
## 2338.5 -1706.6
## PositionPresident & CEO PositionPrincipal Data Architect
## 190867.5 59106.1
## PositionProduction Manager PositionProduction Technician I
## 14645.5 -7928.1
## PositionProduction Technician II PositionSales Manager
## 1424.7 3314.6
## PositionSenior BI Developer PositionShared Services Manager
## 23106.7 28388.5
## PositionSoftware Engineer PositionSoftware Engineering Manager
## 31335.2 14992.2
## PositionSr. Accountant PositionSr. DBA
## 41281.0 41058.8
## PositionSr. Network Engineer HispanicLatinoYes
## 29125.4 -3107.2
## EmpSatisfaction Absences
## 791.3 162.7
## MarriedID1
## -1436.1Result of model comparison using training dataset
Forward: lm(formula = Salary ~ Position + EmpSatisfaction + Absences + HispanicLatino + MarriedID, data = HRtrain) / AIC:4017.63
Backward: lm(formula = Salary ~ Position + Age + HispanicLatino + EmpSatisfaction + Absences, data = HRtrain) / AIC:4018.76
Both: lm(formula = Salary ~ Position + HispanicLatino + EmpSatisfaction + Absences + MarriedID, data = HRtrain) # same as forward / AIC:4017.63
Result of Stepwise Regression
model_forward = lm(formula = Salary ~ Position + EmpSatisfaction + Absences + HispanicLatino + MarriedID, data = HRtrain)
summary(model_forward)##
## Call:
## lm(formula = Salary ~ Position + EmpSatisfaction + Absences +
## HispanicLatino + MarriedID, data = HRtrain)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13567 -4046 0 4276 14585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59675.31 3879.29 15.383 < 2e-16 ***
## PositionAdministrative Assistant -10430.98 4922.83 -2.119 0.03537 *
## PositionArea Sales Manager 1054.27 3679.73 0.287 0.77480
## PositionBI Developer 31874.00 4949.79 6.439 9.21e-10 ***
## PositionBI Director 47432.13 7001.67 6.774 1.45e-10 ***
## PositionCIO 154215.78 6995.10 22.046 < 2e-16 ***
## PositionData Analyst 26402.39 4601.77 5.737 3.65e-08 ***
## PositionData Architect 85475.69 6992.64 12.224 < 2e-16 ***
## PositionDatabase Administrator 49005.56 4924.62 9.951 < 2e-16 ***
## PositionDirector of Operations 104428.43 6990.33 14.939 < 2e-16 ***
## PositionDirector of Sales 115505.20 7004.05 16.491 < 2e-16 ***
## PositionEnterprise Architect 39655.96 6996.47 5.668 5.17e-08 ***
## PositionIT Director 111928.43 6990.33 16.012 < 2e-16 ***
## PositionIT Manager - DB 80015.53 5512.86 14.514 < 2e-16 ***
## PositionIT Manager - Infra 97379.58 7186.40 13.551 < 2e-16 ***
## PositionIT Manager - Support 74605.65 6987.29 10.677 < 2e-16 ***
## PositionIT Support 2338.50 4351.19 0.537 0.59158
## PositionNetwork Engineer -1706.58 4931.97 -0.346 0.72970
## PositionPresident & CEO 190867.55 7187.14 26.557 < 2e-16 ***
## PositionPrincipal Data Architect 59106.13 7142.26 8.276 2.03e-14 ***
## PositionProduction Manager 14645.49 4024.21 3.639 0.00035 ***
## PositionProduction Technician I -7928.06 3533.05 -2.244 0.02596 *
## PositionProduction Technician II 1424.70 3598.81 0.396 0.69263
## PositionSales Manager 3314.63 5518.15 0.601 0.54876
## PositionSenior BI Developer 23106.69 6992.64 3.304 0.00113 **
## PositionShared Services Manager 28388.54 7011.18 4.049 7.43e-05 ***
## PositionSoftware Engineer 31335.22 4169.83 7.515 2.05e-12 ***
## PositionSoftware Engineering Manager 14992.21 6964.98 2.153 0.03259 *
## PositionSr. Accountant 41281.01 5540.25 7.451 2.98e-12 ***
## PositionSr. DBA 41058.80 6999.14 5.866 1.89e-08 ***
## PositionSr. Network Engineer 29125.39 4601.77 6.329 1.67e-09 ***
## EmpSatisfaction 791.28 451.24 1.754 0.08108 .
## Absences 162.66 72.34 2.249 0.02566 *
## HispanicLatinoYes -3107.16 1673.23 -1.857 0.06483 .
## MarriedID1 -1436.11 878.97 -1.634 0.10391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6016 on 194 degrees of freedom
## Multiple R-squared: 0.9598, Adjusted R-squared: 0.9527
## F-statistic: 136.2 on 34 and 194 DF, p-value: < 2.2e-16model_backward = lm(formula = Salary ~ Position + Age + HispanicLatino + EmpSatisfaction + Absences, data = HRtrain)
summary(model_backward)##
## Call:
## lm(formula = Salary ~ Position + Age + HispanicLatino + EmpSatisfaction +
## Absences, data = HRtrain)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13991 -4216 0 3906 15361
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 56691.98 4314.39 13.140 < 2e-16 ***
## PositionAdministrative Assistant -10822.83 4926.94 -2.197 0.029229 *
## PositionArea Sales Manager 476.66 3711.73 0.128 0.897949
## PositionBI Developer 30888.24 4928.33 6.267 2.32e-09 ***
## PositionBI Director 45476.36 7026.80 6.472 7.73e-10 ***
## PositionCIO 154225.83 7016.15 21.982 < 2e-16 ***
## PositionData Analyst 25968.46 4614.11 5.628 6.31e-08 ***
## PositionData Architect 85182.13 7035.10 12.108 < 2e-16 ***
## PositionDatabase Administrator 48230.69 4932.53 9.778 < 2e-16 ***
## PositionDirector of Operations 104691.57 7003.13 14.949 < 2e-16 ***
## PositionDirector of Sales 113327.39 7080.67 16.005 < 2e-16 ***
## PositionEnterprise Architect 38558.89 7094.56 5.435 1.63e-07 ***
## PositionIT Director 111995.08 7008.89 15.979 < 2e-16 ***
## PositionIT Manager - DB 79325.38 5590.05 14.190 < 2e-16 ***
## PositionIT Manager - Infra 95948.02 7161.79 13.397 < 2e-16 ***
## PositionIT Manager - Support 73919.37 7046.12 10.491 < 2e-16 ***
## PositionIT Support 1531.51 4366.85 0.351 0.726184
## PositionNetwork Engineer -2222.90 4935.28 -0.450 0.652918
## PositionPresident & CEO 187313.38 7358.87 25.454 < 2e-16 ***
## PositionPrincipal Data Architect 58250.32 7199.71 8.091 6.34e-14 ***
## PositionProduction Manager 13372.18 4075.93 3.281 0.001227 **
## PositionProduction Technician I -8509.67 3557.98 -2.392 0.017725 *
## PositionProduction Technician II 878.37 3624.53 0.242 0.808773
## PositionSales Manager 3950.94 5530.00 0.714 0.475805
## PositionSenior BI Developer 22682.14 7045.95 3.219 0.001507 **
## PositionShared Services Manager 27398.57 7012.73 3.907 0.000129 ***
## PositionSoftware Engineer 30992.04 4180.01 7.414 3.70e-12 ***
## PositionSoftware Engineering Manager 14189.92 7041.50 2.015 0.045266 *
## PositionSr. Accountant 40115.10 5519.04 7.268 8.68e-12 ***
## PositionSr. DBA 39525.62 7021.48 5.629 6.27e-08 ***
## PositionSr. Network Engineer 27823.63 4685.25 5.939 1.31e-08 ***
## Age 65.50 50.20 1.305 0.193578
## HispanicLatinoYes -2523.93 1661.89 -1.519 0.130463
## EmpSatisfaction 864.27 452.05 1.912 0.057367 .
## Absences 153.75 72.08 2.133 0.034177 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6031 on 194 degrees of freedom
## Multiple R-squared: 0.9596, Adjusted R-squared: 0.9525
## F-statistic: 135.5 on 34 and 194 DF, p-value: < 2.2e-16Check regression assumptions for model_forward, model_backward
#plot(fitted(model_forward),residuals(model_forward),xlab="Fitted values",ylab="Residuals")
#abline(h=0, col="red")
#lines(lowess(model_forward$fitted.values, residuals(model_forward)), col='blue')
#hist(residuals(model_forward), xlab="residuals", col="orange", main=NULL, nclass=15)
#qqPlot(residuals(model_forward), xlab="normal quantiles", ylab="residuals")
#plot(fitted(model_backward),residuals(model_backward),xlab="Fitted values",ylab="Residuals")
#abline(h=0, col="red")
#lines(lowess(model_backward$fitted.values, residuals(model_backward)), col='blue')
# Normality
#hist(residuals(model_backward), xlab="residuals", col="orange", main=NULL, nclass=15)
#qqPlot(residuals(model_backward), xlab="normal quantiles", ylab="residuals")#Regularized Regression
X_pred = cbind(HRtrain$MarriedID, HRtrain$MaritalDesc, HRtrain$Sex, HRtrain$EmploymentStatus, HRtrain$Department, HRtrain$PerfScoreID, HRtrain$RecruitmentSource, HRtrain$Position, HRtrain$State, HRtrain$Age, HRtrain$CitizenDesc, HRtrain$RaceDesc, HRtrain$HispanicLatino, HRtrain$EmployedYear, HRtrain$ManagerName, HRtrain$EngagementSurvey, HRtrain$EmpSatisfaction, HRtrain$SpecialProjectsCount, HRtrain$DaysLateLast30, HRtrain$Absences)
#Lasso Regresssion
smodel.cv = cv.glmnet(X_pred, HRtrain$Salary, alpha = 1, nfolds = 10)
smodel = glmnet(X_pred, HRtrain$Salary, alpha = 1, nlambda=100)
coef(smodel, s = smodel.cv$lambda.min)## 21 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 35059.46413
## V1 .
## V2 .
## V3 .
## V4 .
## V5 .
## V6 2645.67778
## V7 .
## V8 -88.41895
## V9 322.11524
## V10 400.75993
## V11 .
## V12 .
## V13 .
## V14 78.39102
## V15 -167.65175
## V16 .
## V17 427.09593
## V18 5292.39994
## V19 .
## V20 193.83659plot(smodel,xvar="lambda",lwd=2) + abline(v=log(smodel.cv$lambda.min),col="black",lty=2)
## integer(0)Result of Lasso
model_lasso = lm(formula = Salary ~ PerfScoreID + Position + State + Age + EmployedYear + ManagerName + EmpSatisfaction + SpecialProjectsCount + Absences, data = HRtrain)
summary(model_lasso)##
## Call:
## lm(formula = Salary ~ PerfScoreID + Position + State + Age +
## EmployedYear + ManagerName + EmpSatisfaction + SpecialProjectsCount +
## Absences, data = HRtrain)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13917 -2974 0 3370 14353
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 50276.41 10274.10 4.894 2.46e-06 ***
## PerfScoreID 6.05 774.74 0.008 0.99378
## PositionAdministrative Assistant -10649.55 5206.11 -2.046 0.04249 *
## PositionArea Sales Manager 5476.69 7643.14 0.717 0.47473
## PositionBI Developer 31886.08 5176.27 6.160 6.00e-09 ***
## PositionBI Director 47544.52 7527.05 6.316 2.70e-09 ***
## PositionCIO 170433.35 11374.78 14.983 < 2e-16 ***
## PositionData Analyst 26992.01 4871.15 5.541 1.26e-07 ***
## PositionData Architect 85699.92 7274.28 11.781 < 2e-16 ***
## PositionDatabase Administrator 49008.33 5216.64 9.395 < 2e-16 ***
## PositionDirector of Operations 117304.98 9899.52 11.850 < 2e-16 ***
## PositionDirector of Sales 136126.74 12737.34 10.687 < 2e-16 ***
## PositionEnterprise Architect 48624.05 8843.47 5.498 1.55e-07 ***
## PositionIT Director 112391.83 7199.94 15.610 < 2e-16 ***
## PositionIT Manager - DB 80956.34 5988.39 13.519 < 2e-16 ***
## PositionIT Manager - Infra 93847.12 7225.70 12.988 < 2e-16 ***
## PositionIT Manager - Support 74595.62 7261.40 10.273 < 2e-16 ***
## PositionIT Support 4869.09 5210.74 0.934 0.35153
## PositionNetwork Engineer -1560.16 5190.37 -0.301 0.76413
## PositionPresident & CEO 189950.55 11981.11 15.854 < 2e-16 ***
## PositionPrincipal Data Architect 57439.61 7573.25 7.585 2.88e-12 ***
## PositionProduction Manager 26295.72 7888.12 3.334 0.00107 **
## PositionProduction Technician I -13715.80 6214.83 -2.207 0.02879 *
## PositionProduction Technician II -4546.13 6289.69 -0.723 0.47090
## PositionSales Manager 1328.22 11633.94 0.114 0.90925
## PositionSenior BI Developer 23789.89 7375.66 3.225 0.00153 **
## PositionShared Services Manager 40499.86 9836.31 4.117 6.21e-05 ***
## PositionSoftware Engineer 33217.42 4411.68 7.529 3.92e-12 ***
## PositionSoftware Engineering Manager 13987.58 7233.15 1.934 0.05496 .
## PositionSr. Accountant 42584.50 7392.94 5.760 4.39e-08 ***
## PositionSr. DBA 40911.13 7453.34 5.489 1.62e-07 ***
## PositionSr. Network Engineer 32920.25 5448.66 6.042 1.09e-08 ***
## StateAZ -4689.93 8963.52 -0.523 0.60157
## StateCA 10670.11 9090.92 1.174 0.24231
## StateCO 1578.91 8895.82 0.177 0.85936
## StateCT 2099.71 8007.92 0.262 0.79351
## StateFL 2806.73 8930.65 0.314 0.75373
## StateGA 2120.12 8940.00 0.237 0.81285
## StateID -842.00 8922.36 -0.094 0.92494
## StateIN -2391.23 8990.25 -0.266 0.79061
## StateKY -898.22 8925.37 -0.101 0.91997
## StateMA 9781.05 7985.12 1.225 0.22247
## StateME -6531.63 8911.10 -0.733 0.46468
## StateMT -3501.51 8965.76 -0.391 0.69667
## StateNC 4764.88 8940.07 0.533 0.59481
## StateND 1419.66 9112.56 0.156 0.87640
## StateNH 8013.52 8923.89 0.898 0.37059
## StateNV -2788.16 8736.52 -0.319 0.75005
## StateNY 5814.76 8954.69 0.649 0.51707
## StateOH -2660.75 8946.80 -0.297 0.76656
## StateOR -5653.21 8916.89 -0.634 0.52702
## StatePA 12099.44 12546.58 0.964 0.33637
## StateRI NA NA NA NA
## StateTN 7025.72 9008.67 0.780 0.43665
## StateTX 1590.52 8893.21 0.179 0.85829
## StateUT 7850.96 8937.05 0.878 0.38105
## StateVA 9393.67 8992.10 1.045 0.29781
## StateVT 8169.76 8899.30 0.918 0.36003
## StateWA -2445.89 8883.12 -0.275 0.78342
## Age 57.83 60.41 0.957 0.33994
## EmployedYear 105.37 194.85 0.541 0.58944
## ManagerNameAmy Dunn 3113.48 2286.08 1.362 0.17520
## ManagerNameBoard of Directors -7977.34 8893.19 -0.897 0.37110
## ManagerNameBrandon R. LeBlanc NA NA NA NA
## ManagerNameBrannon Miller 4595.41 2325.25 1.976 0.04989 *
## ManagerNameBrian Champaigne NA NA NA NA
## ManagerNameDavid Stanley 2247.37 2383.91 0.943 0.34729
## ManagerNameDebra Houlihan NA NA NA NA
## ManagerNameElijiah Gray 4722.65 2360.18 2.001 0.04714 *
## ManagerNameEric Dougall -2961.62 5860.52 -0.505 0.61403
## ManagerNameJanet King -15323.54 8125.15 -1.886 0.06117 .
## ManagerNameJennifer Zamora NA NA NA NA
## ManagerNameJohn Smith NA NA NA NA
## ManagerNameKelley Spirea 2766.16 2294.09 1.206 0.22974
## ManagerNameKetsia Liebig 904.16 2310.53 0.391 0.69610
## ManagerNameKissy Sullivan 2598.42 2190.08 1.186 0.23726
## ManagerNameLynn Daneault NA NA NA NA
## ManagerNameMichael Albert 2308.66 2436.32 0.948 0.34481
## ManagerNamePeter Monroe NA NA NA NA
## ManagerNameSimon Roup NA NA NA NA
## ManagerNameWebster Butler NA NA NA NA
## EmpSatisfaction 564.66 532.20 1.061 0.29034
## SpecialProjectsCount -574.63 968.30 -0.593 0.55375
## Absences 143.45 80.92 1.773 0.07823 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6166 on 155 degrees of freedom
## Multiple R-squared: 0.9662, Adjusted R-squared: 0.9503
## F-statistic: 60.78 on 73 and 155 DF, p-value: < 2.2e-16Check regression assumptions for model_lasso
#plot(fitted(model_lasso),residuals(model_lasso),xlab="Fitted values",ylab="Residuals")
#abline(h=0, col="red")
#lines(lowess(model_lasso$fitted.values, residuals(model_lasso)), col='blue')
# Normality
#hist(residuals(model_lasso), xlab="residuals", col="orange",main=NULL, nclass=15)
#qqPlot(residuals(model_lasso), xlab="normal quantiles", ylab="residuals")# Ridge Regression
smodel2.cv = cv.glmnet(X_pred, HRtrain$Salary, alpha = 0, nfolds = 10)
smodel2 = glmnet(X_pred, HRtrain$Salary, alpha = 0, nlambda=100)
coef(smodel2, s = smodel2.cv$lambda.min)## 21 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 36411.4559
## V1 220.5822
## V2 107.5987
## V3 1091.1768
## V4 -907.9381
## V5 -1756.0234
## V6 3612.5031
## V7 -135.6206
## V8 -226.3703
## V9 498.5359
## V10 420.5696
## V11 -2273.1762
## V12 -564.6980
## V13 3885.8846
## V14 229.1098
## V15 -323.0585
## V16 909.7769
## V17 1587.1954
## V18 4095.0793
## V19 629.4255
## V20 272.5741plot(smodel2,xvar="lambda",lwd=2) + abline(v=log(smodel2.cv$lambda.min),col="black",lty=2)
## integer(0)model_ridge = glmnet(X_pred, HRtrain$Salary, alpha = 0,family="gaussian")
summary(model_ridge)## Length Class Mode
## a0 100 -none- numeric
## beta 2000 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numericRidge Regression creates full model
# Elastic Net Regresssion
smodel3.cv = cv.glmnet(X_pred, HRtrain$Salary, alpha = 0.5, nfolds = 10)
smodel3 = glmnet(X_pred, HRtrain$Salary, alpha = 0.5, nlambda=100)
coef(smodel3, s = smodel3.cv$lambda.min)## 21 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 46013.40459
## V1 .
## V2 .
## V3 .
## V4 .
## V5 .
## V6 1889.57289
## V7 .
## V8 -51.56577
## V9 83.76383
## V10 299.55245
## V11 .
## V12 .
## V13 .
## V14 .
## V15 -30.22105
## V16 .
## V17 .
## V18 4608.15514
## V19 .
## V20 87.70521plot(smodel3,xvar="lambda",lwd=2) + abline(v=log(smodel3.cv$lambda.min),col="black",lty=2)
## integer(0)#Result of Elastic net
model_net = lm(formula = Salary ~ PerfScoreID + Position + State + Age + EmployedYear + EmpSatisfaction + Absences, data = HRtrain)
summary(model_net)##
## Call:
## lm(formula = Salary ~ PerfScoreID + Position + State + Age +
## EmployedYear + EmpSatisfaction + Absences, data = HRtrain)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13694 -3308 0 3332 14814
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46752.60 9111.10 5.131 7.92e-07 ***
## PerfScoreID -32.11 757.41 -0.042 0.966233
## PositionAdministrative Assistant -10357.43 5168.02 -2.004 0.046672 *
## PositionArea Sales Manager 8502.13 5991.35 1.419 0.157744
## PositionBI Developer 31471.24 5106.11 6.163 5.16e-09 ***
## PositionBI Director 46169.09 7232.26 6.384 1.64e-09 ***
## PositionCIO 154617.36 7203.88 21.463 < 2e-16 ***
## PositionData Analyst 26497.92 4781.96 5.541 1.15e-07 ***
## PositionData Architect 85388.81 7256.62 11.767 < 2e-16 ***
## PositionDatabase Administrator 48609.04 5174.41 9.394 < 2e-16 ***
## PositionDirector of Operations 105018.64 7200.44 14.585 < 2e-16 ***
## PositionDirector of Sales 123903.18 10755.97 11.519 < 2e-16 ***
## PositionEnterprise Architect 47981.41 8288.63 5.789 3.43e-08 ***
## PositionIT Director 112472.36 7189.85 15.643 < 2e-16 ***
## PositionIT Manager - DB 79792.60 5770.39 13.828 < 2e-16 ***
## PositionIT Manager - Infra 93226.35 7165.78 13.010 < 2e-16 ***
## PositionIT Manager - Support 74367.84 7247.82 10.261 < 2e-16 ***
## PositionIT Support 3390.85 4565.14 0.743 0.458664
## PositionNetwork Engineer -2112.21 5126.29 -0.412 0.680843
## PositionPresident & CEO 184211.40 7358.40 25.034 < 2e-16 ***
## PositionPrincipal Data Architect 55948.91 7248.24 7.719 1.02e-12 ***
## PositionProduction Manager 13556.36 4241.99 3.196 0.001668 **
## PositionProduction Technician I -8468.85 3660.77 -2.313 0.021919 *
## PositionProduction Technician II 1000.66 3755.47 0.266 0.790218
## PositionSales Manager 4803.62 10599.67 0.453 0.651004
## PositionSenior BI Developer 22866.32 7270.59 3.145 0.001966 **
## PositionShared Services Manager 28068.36 7206.21 3.895 0.000142 ***
## PositionSoftware Engineer 31489.35 4305.89 7.313 1.04e-11 ***
## PositionSoftware Engineering Manager 13550.02 7218.47 1.877 0.062244 .
## PositionSr. Accountant 39933.53 5640.34 7.080 3.81e-11 ***
## PositionSr. DBA 40041.99 7327.75 5.464 1.66e-07 ***
## PositionSr. Network Engineer 32382.39 5164.26 6.270 2.97e-09 ***
## StateAZ -4191.78 8947.45 -0.468 0.640046
## StateCA 11271.42 9065.96 1.243 0.215512
## StateCO 1937.82 8879.19 0.218 0.827506
## StateCT 1661.23 7935.21 0.209 0.834430
## StateFL 3151.54 8916.02 0.353 0.724182
## StateGA 2589.31 8921.67 0.290 0.772003
## StateID -1170.18 8902.21 -0.131 0.895579
## StateIN -1819.67 8967.33 -0.203 0.839443
## StateKY -384.34 8907.11 -0.043 0.965634
## StateMA 10309.94 7904.10 1.304 0.193899
## StateME -6052.53 8895.26 -0.680 0.497179
## StateMT -2945.41 8947.57 -0.329 0.742428
## StateNC 5189.65 8920.52 0.582 0.561509
## StateND 1516.00 9076.93 0.167 0.867559
## StateNH 8500.62 8905.77 0.955 0.341207
## StateNV -2875.14 8728.00 -0.329 0.742254
## StateNY 6061.48 8936.36 0.678 0.498524
## StateOH -2658.04 8923.99 -0.298 0.766185
## StateOR -5672.81 8901.42 -0.637 0.524807
## StatePA 12051.71 12524.65 0.962 0.337321
## StateRI NA NA NA NA
## StateTN 7165.31 8983.03 0.798 0.426206
## StateTX 1892.75 8877.02 0.213 0.831416
## StateUT 8278.96 8921.87 0.928 0.354778
## StateVA 9444.87 8969.66 1.053 0.293871
## StateVT 8169.50 8881.84 0.920 0.359005
## StateWA -2242.07 8869.43 -0.253 0.800744
## Age 76.75 58.85 1.304 0.193988
## EmployedYear 91.98 183.71 0.501 0.617254
## EmpSatisfaction 496.61 519.29 0.956 0.340295
## Absences 136.27 79.14 1.722 0.086950 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6161 on 167 degrees of freedom
## Multiple R-squared: 0.9637, Adjusted R-squared: 0.9504
## F-statistic: 72.67 on 61 and 167 DF, p-value: < 2.2e-16Elastic net model assumptions test
# Constant Variance / Uncorrelated errors
plot(fitted(model_net),residuals(model_net),xlab="Fitted values",ylab="Residuals")
abline(h=0, col="red")
lines(lowess(model_net$fitted.values, residuals(model_net)), col='blue')
# Normality
hist(residuals(model_net), xlab="residuals", col="orange",main=NULL, nclass=15)
qqPlot(residuals(model_net), xlab="normal quantiles", ylab="residuals")
## 284 264
## 201 188# Linearity
resids_net = model_net$residuals
ggplot(HRtrain, aes(x=PerfScoreID, resids_net)) + geom_point() + geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=Age, resids_net)) + geom_point() + geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=EmployedYear, resids_net)) + geom_point() + geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=EmpSatisfaction, resids_net)) + geom_point() + geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
ggplot(HRtrain, aes(x=Absences, resids_net)) + geom_point() + geom_smooth(method=lm, se=FALSE, fullrange=TRUE)## `geom_smooth()` using formula 'y ~ x'
Variable Selection Criteria
model_position = lm(Salary~Position,data=HRtrain)
n=nrow(HRtrain)
no=c(
nrow(summary(model_full)$coefficients),
nrow(summary(model_backward)$coefficients),
nrow(summary(model_forward)$coefficients),
nrow(summary(model_lasso)$coefficients),
nrow(summary(model_net)$coefficients),
nrow(summary(model_position)$coefficients)
)
rsquared=c(
summary(model_full)$adj.r.squared,
summary(model_backward)$adj.r.squared,
summary(model_forward)$adj.r.squared,
summary(model_lasso)$adj.r.squared,
summary(model_net)$adj.r.squared,
summary(model_position)$adj.r.squared
)
cp=c(
Cp(model_full, S2= summary(model_full)$sigma^2),
Cp(model_backward, S2= summary(model_full)$sigma^2),
Cp(model_forward, S2= summary(model_full)$sigma^2),
Cp(model_lasso, S2= summary(model_full)$sigma^2),
Cp(model_net, S2= summary(model_full)$sigma^2),
Cp(model_position, S2= summary(model_full)$sigma^2)
)
aic=c(
AIC(model_full,k=2),
AIC(model_backward,k=2),
AIC(model_forward,k=2),
AIC(model_lasso,k=2),
AIC(model_net,k=2),
AIC(model_position,k=2)
)
bic=c(
AIC(model_full,k=log(n)),
AIC(model_backward,k=log(n)),
AIC(model_forward,k=log(n)),
AIC(model_lasso,k=log(n)),
AIC(model_net,k=log(n)),
AIC(model_position,k=log(n))
)
criteria = data.frame( "Number of Predictors"=no,"Adjusted R-Squared"=rsquared ,"Mellow Cp"=cp, AIC=aic, BIC=bic)
row.names(criteria)=c("Full Model" , "Stepwise Backward", "Stepwise Forward", "Lasso", "Elastic Net","Position")
criteria## Number.of.Predictors Adjusted.R.Squared Mellow.Cp AIC
## Full Model 97 0.9502852 131.00000 4716.897
## Stepwise Backward 35 0.9525015 26.35127 4670.631
## Stepwise Forward 35 0.9527352 25.43945 4669.501
## Lasso 74 0.9503487 93.80191 4707.386
## Elastic Net 62 0.9504364 63.49194 4700.058
## Position 31 0.9504017 30.53580 4677.211
## BIC
## Full Model 5053.401
## Stepwise Backward 4794.245
## Stepwise Forward 4793.115
## Lasso 4964.916
## Elastic Net 4916.382
## Position 4787.090#Prediction / Testing
predfull = predict.lm(model_full,HRtest)## Warning in predict.lm(model_full, HRtest): prediction from a rank-deficient fit
## may be misleadingpredbackward = predict.lm(model_backward,HRtest)
predforward = predict.lm(model_forward,HRtest)
predlasso = predict.lm(model_lasso,HRtest)## Warning in predict.lm(model_lasso, HRtest): prediction from a rank-deficient fit
## may be misleadingprednet = predict.lm(model_net,HRtest)## Warning in predict.lm(model_net, HRtest): prediction from a rank-deficient fit
## may be misleadingpredpos = predict.lm(model_position,HRtest)
#MSPE
MSPE=c(
mean( (HRtest$Salary - predfull)^2 ),
mean( (HRtest$Salary - predbackward)^2 ),
mean( (HRtest$Salary - predforward)^2 ),
mean( (HRtest$Salary - predlasso)^2 ),
mean( (HRtest$Salary - prednet)^2 ),
mean( (HRtest$Salary - predpos)^2 )
)
#MAE
MAE=c(
mean( abs(HRtest$Salary - predfull) ),
mean( abs(HRtest$Salary - predbackward) ),
mean( abs(HRtest$Salary - predforward) ),
mean( abs(HRtest$Salary - predlasso) ),
mean( abs(HRtest$Salary - prednet) ),
mean( abs(HRtest$Salary - predpos) )
)
#MAPE
MAPE=c(
mean( abs(HRtest$Salary - predfull)/ HRtest$Salary),
mean( abs(HRtest$Salary - predbackward)/ HRtest$Salary),
mean( abs(HRtest$Salary - predforward)/ HRtest$Salary),
mean( abs(HRtest$Salary - predlasso)/ HRtest$Salary),
mean( abs(HRtest$Salary - prednet)/ HRtest$Salary),
mean( abs(HRtest$Salary - predpos)/ HRtest$Salary)
)
#PM
PM =c(
sum( (HRtest$Salary - predfull)^2 ) / sum( (HRtest$Salary - mean(HRtest$Salary) )^2 ),
sum( (HRtest$Salary - predbackward)^2 ) / sum( (HRtest$Salary - mean(HRtest$Salary) )^2 ),
sum( (HRtest$Salary - predforward)^2 ) / sum( (HRtest$Salary - mean(HRtest$Salary) )^2 ),
sum( (HRtest$Salary - predlasso)^2 ) / sum( (HRtest$Salary - mean(HRtest$Salary) )^2 ),
sum( (HRtest$Salary - prednet)^2 ) / sum( (HRtest$Salary - mean(HRtest$Salary) )^2 ),
sum( (HRtest$Salary - predpos)^2 ) / sum( (HRtest$Salary - mean(HRtest$Salary) )^2 )
)
performance = data.frame( MSPE=MSPE ,MAE=MAE, MAPE=MAPE, PM=PM)
row.names(performance)=c("Full Model" , "Stepwise Backward", "Stepwise Forward", "Lasso", "Elastic Net","Position")
performance## MSPE MAE MAPE PM
## Full Model 55078189 5456.196 0.09313196 0.06679844
## Stepwise Backward 39726001 4816.832 0.08141588 0.04817941
## Stepwise Forward 43125080 5047.964 0.08492801 0.05230179
## Lasso 39829696 4756.452 0.08021371 0.04830517
## Elastic Net 39309436 4686.834 0.07868069 0.04767420
## Position 40701550 5132.405 0.08545892 0.04936255Check Multicollinearity
df <- data.frame(HRtrain$PerfScoreID, HRtrain$Age ,HRtrain$EmployedYear, HRtrain$EmpSatisfaction, HRtrain$Absences, HRtrain$Salary)
cor(df)## HRtrain.PerfScoreID HRtrain.Age HRtrain.EmployedYear
## HRtrain.PerfScoreID 1.00000000 0.09855999 0.115053177
## HRtrain.Age 0.09855999 1.00000000 -0.021298729
## HRtrain.EmployedYear 0.11505318 -0.02129873 1.000000000
## HRtrain.EmpSatisfaction 0.29842552 -0.05334735 0.009218585
## HRtrain.Absences 0.07994592 -0.03712606 0.002687228
## HRtrain.Salary 0.14596085 0.17524732 0.025044339
## HRtrain.EmpSatisfaction HRtrain.Absences HRtrain.Salary
## HRtrain.PerfScoreID 0.298425521 0.079945922 0.14596085
## HRtrain.Age -0.053347349 -0.037126061 0.17524732
## HRtrain.EmployedYear 0.009218585 0.002687228 0.02504434
## HRtrain.EmpSatisfaction 1.000000000 0.089681478 0.09190903
## HRtrain.Absences 0.089681478 1.000000000 0.09301789
## HRtrain.Salary 0.091909031 0.093017889 1.00000000cat("VIF Threshold for model_net:", max(10, 1/(1-summary(model_net)$r.squared)), "\n")## VIF Threshold for model_net: 27.54581HRmulti<- HRtrain[-c(221),]
model_multi= lm(formula = Salary ~ PerfScoreID + State + Position + Age + EmployedYear + EmpSatisfaction + Absences, data = HRmulti)
summary(model_multi)##
## Call:
## lm(formula = Salary ~ PerfScoreID + State + Position + Age +
## EmployedYear + EmpSatisfaction + Absences, data = HRmulti)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13694 -3353 0 3339 14814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46752.60 9111.10 5.131 7.92e-07 ***
## PerfScoreID -32.11 757.41 -0.042 0.966233
## StateAZ -4191.78 8947.45 -0.468 0.640046
## StateCA 11271.42 9065.96 1.243 0.215512
## StateCO 1937.82 8879.19 0.218 0.827506
## StateCT 1661.23 7935.21 0.209 0.834430
## StateFL 3151.54 8916.02 0.353 0.724182
## StateGA 2589.31 8921.67 0.290 0.772003
## StateID -1170.18 8902.21 -0.131 0.895579
## StateIN -1819.67 8967.33 -0.203 0.839443
## StateKY -384.34 8907.11 -0.043 0.965634
## StateMA 10309.94 7904.10 1.304 0.193899
## StateME -6052.53 8895.26 -0.680 0.497179
## StateMT -2945.41 8947.57 -0.329 0.742428
## StateNC 5189.65 8920.52 0.582 0.561509
## StateND 1516.00 9076.93 0.167 0.867559
## StateNH 8500.62 8905.77 0.955 0.341207
## StateNV -2875.14 8728.00 -0.329 0.742254
## StateNY 6061.48 8936.36 0.678 0.498524
## StateOH -2658.04 8923.99 -0.298 0.766185
## StateOR -5672.81 8901.42 -0.637 0.524807
## StatePA 12051.71 12524.65 0.962 0.337321
## StateTN 7165.31 8983.03 0.798 0.426206
## StateTX 1892.75 8877.02 0.213 0.831416
## StateUT 8278.96 8921.87 0.928 0.354778
## StateVA 9444.87 8969.66 1.053 0.293871
## StateVT 8169.50 8881.84 0.920 0.359005
## StateWA -2242.07 8869.43 -0.253 0.800744
## PositionAdministrative Assistant -10357.43 5168.02 -2.004 0.046672 *
## PositionArea Sales Manager 8502.13 5991.35 1.419 0.157744
## PositionBI Developer 31471.24 5106.11 6.163 5.16e-09 ***
## PositionBI Director 46169.09 7232.26 6.384 1.64e-09 ***
## PositionCIO 154617.36 7203.88 21.463 < 2e-16 ***
## PositionData Analyst 26497.92 4781.96 5.541 1.15e-07 ***
## PositionData Architect 85388.81 7256.62 11.767 < 2e-16 ***
## PositionDatabase Administrator 48609.04 5174.41 9.394 < 2e-16 ***
## PositionDirector of Operations 105018.64 7200.44 14.585 < 2e-16 ***
## PositionEnterprise Architect 47981.41 8288.63 5.789 3.43e-08 ***
## PositionIT Director 112472.36 7189.85 15.643 < 2e-16 ***
## PositionIT Manager - DB 79792.60 5770.39 13.828 < 2e-16 ***
## PositionIT Manager - Infra 93226.35 7165.78 13.010 < 2e-16 ***
## PositionIT Manager - Support 74367.84 7247.82 10.261 < 2e-16 ***
## PositionIT Support 3390.85 4565.14 0.743 0.458664
## PositionNetwork Engineer -2112.21 5126.29 -0.412 0.680843
## PositionPresident & CEO 184211.40 7358.40 25.034 < 2e-16 ***
## PositionPrincipal Data Architect 55948.91 7248.24 7.719 1.02e-12 ***
## PositionProduction Manager 13556.36 4241.99 3.196 0.001668 **
## PositionProduction Technician I -8468.85 3660.77 -2.313 0.021919 *
## PositionProduction Technician II 1000.66 3755.47 0.266 0.790218
## PositionSales Manager 4803.62 10599.67 0.453 0.651004
## PositionSenior BI Developer 22866.32 7270.59 3.145 0.001966 **
## PositionShared Services Manager 28068.36 7206.21 3.895 0.000142 ***
## PositionSoftware Engineer 31489.35 4305.89 7.313 1.04e-11 ***
## PositionSoftware Engineering Manager 13550.02 7218.47 1.877 0.062244 .
## PositionSr. Accountant 39933.53 5640.34 7.080 3.81e-11 ***
## PositionSr. DBA 40041.99 7327.75 5.464 1.66e-07 ***
## PositionSr. Network Engineer 32382.39 5164.26 6.270 2.97e-09 ***
## Age 76.75 58.85 1.304 0.193988
## EmployedYear 91.98 183.71 0.501 0.617254
## EmpSatisfaction 496.61 519.29 0.956 0.340295
## Absences 136.27 79.14 1.722 0.086950 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6161 on 167 degrees of freedom
## Multiple R-squared: 0.961, Adjusted R-squared: 0.947
## F-statistic: 68.58 on 60 and 167 DF, p-value: < 2.2e-16summary(model_multi)$r.squared## [1] 0.9609993vif(model_multi)## GVIF Df GVIF^(1/(2*Df))
## PerfScoreID 1.419725 1 1.191522
## State 239.370545 26 1.111095
## Position 235.676296 29 1.098758
## Age 1.641773 1 1.281317
## EmployedYear 1.612118 1 1.269692
## EmpSatisfaction 1.456007 1 1.206651
## Absences 1.331421 1 1.153872Outlier Analysis
#Cook's Distance Analysis
cook = cooks.distance(model_multi)
#Rule of Thumb
alarm = 4/nrow(HRtrain)
plot(cook,type="h",lwd=3,col="red", ylab = "Cook's Distance")
abline(h = alarm,col="red") 
#Rule of Thumb
alarm = 4/nrow(HRtrain)
cat("Observation", which(cook>alarm), "has a cook's distance that is greater than", alarm)## Observation 41 120 133 172 173 186 201 212 has a cook's distance that is greater than 0.01746725# Check Results from Outlier Removal
HRtrain[201,]## MarriedID MaritalDesc Sex EmploymentStatus Department PerfScoreID
## 284 1 Married M Voluntarily Terminated IT/IS 3
## RecruitmentSource Salary Position State Age CitizenDesc RaceDesc
## 284 CareerBuilder 75281 Network Engineer MA 33 US Citizen White
## HispanicLatino EmployedYear ManagerName EngagementSurvey EmpSatisfaction
## 284 No 1 Peter Monroe 5 3
## SpecialProjectsCount DaysLateLast30 Absences
## 284 5 0 11model_net_o = lm(formula = Salary ~ PerfScoreID + Position + State + Age + EmployedYear + EmpSatisfaction + Absences, data = HRtrain[-201,])
summary(model_net)##
## Call:
## lm(formula = Salary ~ PerfScoreID + Position + State + Age +
## EmployedYear + EmpSatisfaction + Absences, data = HRtrain)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13694 -3308 0 3332 14814
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46752.60 9111.10 5.131 7.92e-07 ***
## PerfScoreID -32.11 757.41 -0.042 0.966233
## PositionAdministrative Assistant -10357.43 5168.02 -2.004 0.046672 *
## PositionArea Sales Manager 8502.13 5991.35 1.419 0.157744
## PositionBI Developer 31471.24 5106.11 6.163 5.16e-09 ***
## PositionBI Director 46169.09 7232.26 6.384 1.64e-09 ***
## PositionCIO 154617.36 7203.88 21.463 < 2e-16 ***
## PositionData Analyst 26497.92 4781.96 5.541 1.15e-07 ***
## PositionData Architect 85388.81 7256.62 11.767 < 2e-16 ***
## PositionDatabase Administrator 48609.04 5174.41 9.394 < 2e-16 ***
## PositionDirector of Operations 105018.64 7200.44 14.585 < 2e-16 ***
## PositionDirector of Sales 123903.18 10755.97 11.519 < 2e-16 ***
## PositionEnterprise Architect 47981.41 8288.63 5.789 3.43e-08 ***
## PositionIT Director 112472.36 7189.85 15.643 < 2e-16 ***
## PositionIT Manager - DB 79792.60 5770.39 13.828 < 2e-16 ***
## PositionIT Manager - Infra 93226.35 7165.78 13.010 < 2e-16 ***
## PositionIT Manager - Support 74367.84 7247.82 10.261 < 2e-16 ***
## PositionIT Support 3390.85 4565.14 0.743 0.458664
## PositionNetwork Engineer -2112.21 5126.29 -0.412 0.680843
## PositionPresident & CEO 184211.40 7358.40 25.034 < 2e-16 ***
## PositionPrincipal Data Architect 55948.91 7248.24 7.719 1.02e-12 ***
## PositionProduction Manager 13556.36 4241.99 3.196 0.001668 **
## PositionProduction Technician I -8468.85 3660.77 -2.313 0.021919 *
## PositionProduction Technician II 1000.66 3755.47 0.266 0.790218
## PositionSales Manager 4803.62 10599.67 0.453 0.651004
## PositionSenior BI Developer 22866.32 7270.59 3.145 0.001966 **
## PositionShared Services Manager 28068.36 7206.21 3.895 0.000142 ***
## PositionSoftware Engineer 31489.35 4305.89 7.313 1.04e-11 ***
## PositionSoftware Engineering Manager 13550.02 7218.47 1.877 0.062244 .
## PositionSr. Accountant 39933.53 5640.34 7.080 3.81e-11 ***
## PositionSr. DBA 40041.99 7327.75 5.464 1.66e-07 ***
## PositionSr. Network Engineer 32382.39 5164.26 6.270 2.97e-09 ***
## StateAZ -4191.78 8947.45 -0.468 0.640046
## StateCA 11271.42 9065.96 1.243 0.215512
## StateCO 1937.82 8879.19 0.218 0.827506
## StateCT 1661.23 7935.21 0.209 0.834430
## StateFL 3151.54 8916.02 0.353 0.724182
## StateGA 2589.31 8921.67 0.290 0.772003
## StateID -1170.18 8902.21 -0.131 0.895579
## StateIN -1819.67 8967.33 -0.203 0.839443
## StateKY -384.34 8907.11 -0.043 0.965634
## StateMA 10309.94 7904.10 1.304 0.193899
## StateME -6052.53 8895.26 -0.680 0.497179
## StateMT -2945.41 8947.57 -0.329 0.742428
## StateNC 5189.65 8920.52 0.582 0.561509
## StateND 1516.00 9076.93 0.167 0.867559
## StateNH 8500.62 8905.77 0.955 0.341207
## StateNV -2875.14 8728.00 -0.329 0.742254
## StateNY 6061.48 8936.36 0.678 0.498524
## StateOH -2658.04 8923.99 -0.298 0.766185
## StateOR -5672.81 8901.42 -0.637 0.524807
## StatePA 12051.71 12524.65 0.962 0.337321
## StateRI NA NA NA NA
## StateTN 7165.31 8983.03 0.798 0.426206
## StateTX 1892.75 8877.02 0.213 0.831416
## StateUT 8278.96 8921.87 0.928 0.354778
## StateVA 9444.87 8969.66 1.053 0.293871
## StateVT 8169.50 8881.84 0.920 0.359005
## StateWA -2242.07 8869.43 -0.253 0.800744
## Age 76.75 58.85 1.304 0.193988
## EmployedYear 91.98 183.71 0.501 0.617254
## EmpSatisfaction 496.61 519.29 0.956 0.340295
## Absences 136.27 79.14 1.722 0.086950 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6161 on 167 degrees of freedom
## Multiple R-squared: 0.9637, Adjusted R-squared: 0.9504
## F-statistic: 72.67 on 61 and 167 DF, p-value: < 2.2e-16summary(model_net_o)##
## Call:
## lm(formula = Salary ~ PerfScoreID + Position + State + Age +
## EmployedYear + EmpSatisfaction + Absences, data = HRtrain[-201,
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -14023 -3158 0 3160 13116
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45885.92 8893.98 5.159 7.00e-07 ***
## PerfScoreID -296.87 744.00 -0.399 0.69040
## PositionAdministrative Assistant -9868.01 5044.84 -1.956 0.05214 .
## PositionArea Sales Manager 8617.25 5845.75 1.474 0.14235
## PositionBI Developer 31823.86 4983.24 6.386 1.64e-09 ***
## PositionBI Director 46261.17 7056.41 6.556 6.69e-10 ***
## PositionCIO 154709.73 7028.73 22.011 < 2e-16 ***
## PositionData Analyst 26867.67 4667.21 5.757 4.05e-08 ***
## PositionData Architect 85791.69 7081.33 12.115 < 2e-16 ***
## PositionDatabase Administrator 49028.43 5050.40 9.708 < 2e-16 ***
## PositionDirector of Operations 105058.53 7025.32 14.954 < 2e-16 ***
## PositionDirector of Sales 124616.42 10496.92 11.872 < 2e-16 ***
## PositionEnterprise Architect 48453.73 8088.49 5.990 1.26e-08 ***
## PositionIT Director 112633.02 7015.17 16.056 < 2e-16 ***
## PositionIT Manager - DB 79879.31 5630.11 14.188 < 2e-16 ***
## PositionIT Manager - Infra 93062.86 6991.69 13.310 < 2e-16 ***
## PositionIT Manager - Support 74501.28 7071.67 10.535 < 2e-16 ***
## PositionIT Support 3614.08 4454.70 0.811 0.41836
## PositionNetwork Engineer -9618.10 5566.90 -1.728 0.08590 .
## PositionPresident & CEO 183982.78 7179.81 25.625 < 2e-16 ***
## PositionPrincipal Data Architect 56362.21 7073.22 7.968 2.45e-13 ***
## PositionProduction Manager 13810.04 4139.64 3.336 0.00105 **
## PositionProduction Technician I -8370.60 3571.87 -2.343 0.02029 *
## PositionProduction Technician II 1220.11 3664.83 0.333 0.73961
## PositionSales Manager 5228.30 10342.79 0.506 0.61388
## PositionSenior BI Developer 23251.29 7094.85 3.277 0.00128 **
## PositionShared Services Manager 28494.87 7032.31 4.052 7.78e-05 ***
## PositionSoftware Engineer 31656.59 4201.51 7.535 3.00e-12 ***
## PositionSoftware Engineering Manager 13268.22 7043.49 1.884 0.06135 .
## PositionSr. Accountant 39843.80 5503.23 7.240 1.59e-11 ***
## PositionSr. DBA 40693.16 7152.67 5.689 5.64e-08 ***
## PositionSr. Network Engineer 32615.22 5039.22 6.472 1.04e-09 ***
## StateAZ -3581.33 8732.09 -0.410 0.68224
## StateCA 11984.08 8848.50 1.354 0.17746
## StateCO 2438.31 8664.76 0.281 0.77875
## StateCT 1953.98 7742.79 0.252 0.80107
## StateFL 3683.65 8700.89 0.423 0.67258
## StateGA 3020.17 8705.80 0.347 0.72909
## StateID -843.13 8686.34 -0.097 0.92279
## StateIN -980.76 8753.49 -0.112 0.91093
## StateKY 65.03 8691.69 0.007 0.99404
## StateMA 10919.91 7714.41 1.416 0.15879
## StateME -5511.89 8680.69 -0.635 0.52633
## StateMT -2451.12 8731.42 -0.281 0.77927
## StateNC 5936.96 8706.95 0.682 0.49627
## StateND 1671.56 8856.31 0.189 0.85053
## StateNH 9097.62 8691.33 1.047 0.29674
## StateNV -2830.71 8515.72 -0.332 0.74000
## StateNY 6426.09 8719.81 0.737 0.46219
## StateOH -1745.90 8712.00 -0.200 0.84141
## StateOR -5044.74 8687.33 -0.581 0.56223
## StatePA 12542.08 12221.07 1.026 0.30626
## StateRI NA NA NA NA
## StateTN 8120.43 8770.06 0.926 0.35583
## StateTX 2626.01 8664.39 0.303 0.76221
## StateUT 8936.37 8707.50 1.026 0.30625
## StateVA 10266.95 8755.59 1.173 0.24263
## StateVT 8513.76 8666.54 0.982 0.32735
## StateWA -2001.90 8654.06 -0.231 0.81735
## Age 85.70 57.49 1.491 0.13796
## EmployedYear 165.79 180.84 0.917 0.36059
## EmpSatisfaction 549.99 506.96 1.085 0.27955
## Absences 121.81 77.36 1.574 0.11728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6011 on 166 degrees of freedom
## Multiple R-squared: 0.9656, Adjusted R-squared: 0.953
## F-statistic: 76.49 on 61 and 166 DF, p-value: < 2.2e-16